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Executive Summary

Lessons learned, related information, department of transportation case study: enterprise data inventory.

In response to the Open Government Directive, DOT developed a strategic action plan to inventory and release high-value information through the Data.gov portal. The Department sustained efforts in building its data inventory, responding to the President’s memorandum on regulatory compliance with a comprehensive plan that was recognized as a model for other agencies to follow.

Department of Transportation

Case studies & examples

Originally published January 8, 2013

Open Data at DOT - A Case Study for Maturing Data Release Practices to Drive Innovation and Increase Accountability

Kristen Baldwin, Associate Chief Information Officer for IT Policy Oversight Office of the Chief Information Officer, Department of Transportation

The DOT performs a wide range of business services aimed at solving the complex safety issues associated with our Nation’s transportation systems. The DOT relies heavily on its data resources to drive business and investment decisions. These include data compiled by State, local, and tribal governments, as well as private sector entities. The Department is committed to unlocking more public data to:

• Drive innovation by tapping into the ingenuity of the American people;
• Increase agency accountability; and
• Solidify the connection between the Department’s services and individual citizens, business, governmental bodies, universities, and other non-government organizations.

In response to the Open Government Directive , DOT developed a strategic action plan to inventory and release high-value information through the Data.gov portal. In the winter of 2010, the DOT Associate Chief Information Officer for IT Policy Oversight, serving as the Senior Accountable Official for Open Government, convened a wide-ranging group of stakeholders from around the Department to develop the required strategic action plan. The plan was detailed in DOT’s Open Government Plan, version 1.2 (released on June 25, 2010), and included three tracks:

• Strategy : In the near-term, transform DOT’s data release posture and in the long-term, sustain openness.
• Policy : Provide guidelines for identifying datasets and prioritizing them for release. Institute standing policies for approving, publishing, and managing releases.
• Inventory : Build list of source information systems using budget exhibits. Compare these against data already released on websites and against information collections. Utilize these lists to develop a complete inventory of datasets, prioritize it, and sequence data for release.

DOT detailed the results of its strategic action plan on its open government Web page . The Department sustained efforts in building its data inventory, responding to the President’s memorandum on regulatory compliance with a comprehensive plan that was recognized as a model for other agencies to follow.

The DOT has a long-standing tradition of making data available to the public. As a partner agency involved in the stand-up of Fedstats.gov, now USA.gov , the Bureau of Transportation Statistics helped to lead the development of an important pre-cursor to Data.gov . In addition, the DOT operates a number of transparency portals that provide information on railroad safety , commercial motor vehicle safety , vehicle safety , and pipeline safety . The Department’s leaders identified a number of fundamental issues that must be considered in order to provide sustained access to high-value transportation data. Many of these issues are being addressed and managed through internal policies. These issues include:

• Determining a process to release data and contextualize that data;
• Creating a data inventory and selecting the right datasets to release;
• Determining how to approach developing a DOT-wide data architecture;
• Providing data in a format usable by industry and individuals, consistent with security, privacy and confidentiality interests;
• Portioning and organizing the data so that it is manageable and searchable; and
• Maintaining the quality of the data and creating and maintaining a dialogue with stakeholders about changes in data availability.

To achieve the objectives outlined in the Open Government Directive and address the concerns outlined above, DOT developed a strategic action plan, outlined below:

The plan contained three primary tracks. The Strategy track provided overarching guidance for the Department’s approach to information access. In the short term, DOT’s strategy focused on achieving the open data objectives outlined in the Open Government Directive . The Department developed longer-term information access strategy that addresses the methods for creating enduring access to the Department’s data. These strategies are underpinned by policies focused on approving and publishing data and the necessary procedures to managing those data releases. Finally, the Department met Open Government Directive requirements for identifying and releasing high value data using a structured, standard process for creating its data inventory .

The DOT data inventory process involved a review of the Department’s information technology (IT) portfolio and data that is already released on DOT Web sites. Information that was published in an unstructured format (for instance, embedded in reports) was traced back to its source information system wherever possible. This step was necessary to ensure that the release of underlying structured data (for instance, data that are included in tables or used to generate graphs inside a report) could be automated from an authoritative source. Further, the Department reviewed its active and expired information collections (authorized under the Paperwork Reduction Act (PRA)) and its Electronic Information Systems inventory (required to meet Federal records management requirements) to trace these items back to the appropriate authoritative information systems. To assist in maintaining this inventory, DOT leveraged its Enterprise Architecture to develop a registry. The DOT Chief Enterprise Architects also developed a structured process to identify and prioritize datasets for release.

The DOT’s Open Government Policy Working Group, that was convened to recommend a policy development strategy to senior leadership, consisted of subject matter experts from policy, budget, performance, strategic planning, human resources, technology operations, and legal. Developing a unified Open Government policy for the DOT was a large task. Some major policy areas were well understood, including policies around data transparency and the selection and use of social media tools. However, to create a policy framework that drives towards the DOT’s strategic goals for Open Government, a unified policy DOT considered more than those well-understood areas. The figure to the right outlines DOT’s approach to an open government policy framework.

Once the stage was set for employees to understand what tools are available to increase public engagement in their daily work, the Department explored establishing processes for decision-makers to prioritize the Department’s resources in posting information. Those processes ensured that information posted is “releasable” and “usable” both in content and format.

Content releasability is determined with value, quality, security, privacy, and confidentiality interests in mind. Format guidelines differ depending on whether the information is structured or unstructured. The DOT will continue to release DOT data in a timely manner by proactively making it available online in consistent, open formats, while assuring accuracy and protecting privacy, security, and confidentiality.

The DOT has released 765 datasets or tools to Data.gov. The processes and policies that have been established under DOT’s approach to Open Government helped DOT build a comprehensive plan responding to the President’s memorandum on regulatory compliance that was recognized as a model for other agencies to follow. In May of 2012, DOT, in partnership with the Department of Justice, Department of Labor, and the Consumer Product Safety Commission, launched Safety.Data.gov with 713 datasets, four (4) mobile apps, 14 resources and public domain software tools, and three (3) challenges or competitions. In September 2012, we held the first safety datapalooza, and the community grew to 869 datasets, 10 mobile apps, and six (6) challenges or competitions encouraging public use of safety data from around the government.

DOT has already recognized benefits as a result of regulatory transparency efforts. For example, for the first time, FMCSA has begun releasing the information behind its Safety Measurement System (SMS) to the public in bulk, downloadable formats. Business and private citizens are mining this data for potential opportunities. For instance, a windshield repair company looking to grow its business is mining FMCSA information about safety violations to identify new customers. DOT and the public realize a benefit because the commercial vehicle operator customer is using a safer vehicle and operating in compliance with Federal Motor Carrier Safety Regulations. In addition, DOT has pursued innovation in its data release practices:

• FMCSA released an API and mobile application to help people easily access a bus company’s safety performance record, file a complaint and more.
• FRA released public access to its safety data APIs .
• Transparency, collaboration, and participation depend upon information which is available, easy to find, and easily manipulated, aggregated, and/or re-published. While this task is daunting for an enterprise such as the DOT, it can be achieved incrementally. Getting a handle on the data inventory and ensuring that the public can find that information is an important first step, and improvements in release practices can be prioritized as a second step.
• While CIOs have a great detail of statutory authority and responsibility for open data, they must engage with policymakers and business process owners to develop a complete inventory of datasets and understand their releasability. In developing the Department’s response to the President’s memorandum on regulatory compliance, the CIO collaborated closely with the Office of General Counsel. Similarly, in building the safety community on Data.gov, the CIO collaborated closely with the DOT Safety Council.
• Ensuring that all of the DOT datasets were listed on Data.gov provided a solid foundation for further innovation. DOT took the vision for the safety community on Data.gov from concept to functioning prototype in just five months. The existing data releases helped us frame the foundational categories and taxonomy for the community, and it enabled us to quickly bring other Federal agencies on board. The shared, centralized platform of Data.gov was essential to this rapid development process.
• DOT Open Government
• DOT Data Inventory
• DOT Regulatory Compliance Plan
• DOT on Data.gov

References to the product and/or service names of the hardware and/or software products used in this case study do not constitute an endorsement of such hardware and/or software products.

resources.data.gov

An official website of the Office of Management and Budget, the General Services Administration, and the Office of Government Information Services.

This section contains explanations of common terms referenced on resources.data.gov.

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Home › Case Studies › Unlocking Insights: Transportation Data Analytics for Smarter Mobility

Unlocking Insights: Transportation Data Analytics for Smarter Mobility

Written By: Sudeshna Ghosh

In today’s fast-paced global economy, transportation data analytics plays a pivotal role in driving operational efficiency, optimizing logistics, and enhancing customer satisfaction. By harnessing advanced analytics techniques, businesses can gain invaluable insights into transportation trends, route optimization, fleet management, and supply chain performance.

Quantzig’s Transportation Data Analytics Dashboard solution offers a comprehensive suite of tools and capabilities to help clients leverage their transportation data effectively. With real-time data visualization, predictive analytics, and performance monitoring features, our solution empowers businesses to make informed decisions, streamline operations, and mitigate risks in the transportation ecosystem. From route planning and vehicle tracking to demand forecasting and cost optimization, our transportation data analytics dashboard enables companies to unlock actionable insights, enhance decision-making processes, and drive sustainable growth in a competitive market environment. 

Request a demo to experience the meaningful insights we derive from data through our analytical tools and platform capabilities. Schedule a demo today!  

What You’ll Find in this Case Study:

• Engagement Overview
• Transportation Analytics Business Challenge

Transportation Analytics Solution Offered and Value Delivered

• Benefits of Transportation Analytics

Quantzig’s Success Story:

Client Details

Headquartered in Denmark, the client is a leading consumer packaged goods  CPG manufacturer whose private-label products are distributed to several retail outlets across the globe. To sustain a competitive edge, the client wanted to deploy the right logistics management systems and efficient processes to transport their products to the end-users. Though they had an extensive delivery team spread across geographies to connect their warehouses and partner outlets, they faced major roadblocks in transporting their goods promptly.

Challenges Faced by the Client

Data obtained from every source can help you unearth actionable insights if analyzed accurately, and transportation data is no exception. Having said that, it’s crucial to note that transportation data analytics has the potential to improve logistics management, as well as enable businesses to optimize transit routes and services. Leveraging transportation  analytics  to develop route maps can help businesses optimize the logistic workflow, which in turn, will result in a drastic reduction in congestion levels and time spent in transit.

The manufacturer’s delivery team was complex and comprised of hundreds of employees and partners. The use of outmoded logistics tracking systems made it difficult to track, update, monitor, and deliver products in a timely manner. Also, the outdated legacy systems were difficult to maintain and proved to be of no use in logistics management. Such factors along with the ongoing loss made it crucial for the client to replace the legacy system with a more efficient logistics workflow.

The CPG manufacturer approached Quantzig to leverage its expertise in transportation data analytics and drive significant improvements in the logistics workflow. Our transportation analytics solutions helped the client to gain in-depth insights into their logistics routes through an advanced backend dashboard that provided real-time insights based on transportation data.

Through our transportation analytics solutions we helped the client to revamp their current logistics workflow and achieve the desired level of efficiency. In addition, it’s essential to note that transportation analytics improved their logistics and route planning capability and helped them achieve huge savings on maintenance and renewal.

Experience the advantages firsthand by testing a customized complimentary pilot designed to address your specific requirements. Pilot studies are non-committal in nature.  

Introduction to Transportation Data Analytics

Transportation data analytics involves the collection, analysis, and interpretation of data related to transportation systems. It helps identify patterns and trends in transportation behavior, such as traffic congestion or public transport usage. Data analytics can be used to optimize transportation routes and schedules for efficiency and sustainability. It plays a crucial role in improving safety by identifying accidents-prone areas and suggesting targeted interventions. Transportation data analytics can also aid in predicting demand, optimizing pricing strategies , and enhancing overall user experience in transportation services. 

Importance and Benefits of Transportation Data Analytics

Transportation data analytics offers numerous benefits to the transportation industry, leveraging data analytics, Big Data , AI, and alternative data sources to optimize operations, enhance decision-making, and capitalize on opportunities. Some of the key benefits include:

1. Capacity Constraints Management:

Through transportation data analytics, organizations can gain insights into capacity constraints within their networks, enabling them to optimize routes and resources efficiently. By analyzing historical data and real-time information, companies can proactively address bottlenecks, minimize delays, and ensure timely delivery of goods. This optimization not only enhances operational efficiency but also improves customer satisfaction by meeting demand effectively, ultimately driving competitive advantage in the market. 

2. Mergers and Acquisitions Support:

Transportation data analytics plays a crucial role in supporting organizations undergoing mergers and acquisitions by facilitating the seamless integration of transportation operations. By harmonizing data from disparate systems and platforms, companies can streamline processes, reduce redundancies, and achieve synergies more effectively. This analysis ensures continuity of operations, minimizes disruptions, and accelerates the integration process, ultimately maximizing the value derived from mergers and acquisitions. 

3. Freight Spending Optimization:

Leveraging transportation data analytics, organizations can optimize freight spending by identifying cost-saving opportunities and negotiating favorable rates with carriers. By analyzing freight spending data, companies can identify inefficiencies, consolidate shipments, and optimize transportation modes to reduce overall spending. This optimization not only improves cost-effectiveness but also enhances profitability, enabling organizations to allocate resources more efficiently and invest in strategic initiatives for growth and innovation. 

4. Leadership Decision Support:

Transportation data analytics empowers organizational leaders to make informed decisions backed by data insights. By leveraging optimization techniques and predictive analytics , leaders can align transportation strategies with overall business objectives, driving strategic alignment and operational excellence. This data-driven approach enables proactive decision-making, fosters agility in response to market changes, and positions organizations for long-term success in a competitive landscape. 

5. Utilization of Big Data:

Harnessing the power of big data, transportation data analytics uncovers patterns and trends that were previously inaccessible. By analyzing vast datasets from various sources, including GPS data, sensor data, and geospatial data, organizations gain actionable insights for proactive decision-making and strategy formulation. This utilization of big data enables companies to anticipate market trends, identify emerging opportunities, and mitigate risks, ultimately driving competitive advantage and sustained growth in the transportation industry. 

6. Improving Road Safety with Data Analytics 

It entails leveraging advanced technologies and vast datasets to identify and mitigate risks on roadways. By analyzing historical accident data, traffic patterns, and driver behavior, organizations can identify high-risk areas, implement targeted interventions, and improve overall road safety. Additionally, real-time monitoring and predictive analytics enable proactive measures such as adaptive traffic signal control and automated collision avoidance systems, further enhancing safety for drivers, pedestrians, and cyclists alike. 

7. Enhancing Efficiency in Public Transportation Systems through Data Analytics 

It involves optimizing routes, schedules, and resource allocation to improve service quality and reliability. By analyzing passenger demand, travel patterns, and operational performance metrics, transportation authorities can identify opportunities for route optimization, frequency adjustments, and capacity planning. Moreover, predictive maintenance and real-time monitoring enable proactive management of fleet assets, reducing downtime and ensuring seamless operations. Through data-driven insights and continuous optimization efforts, public transportation systems can provide more efficient, reliable, and sustainable mobility solutions for urban populations. 

Types of Transportation Data Collected

In the dynamic field of transportation logistics, a wide range of data types are collected to facilitate informed decision-making, optimize operational processes, and enhance overall supply chain efficiency. These data types span from traditional metrics such as transport volumes, transit times, and associated costs to more sophisticated datasets, including real-time GPS tracking, sensor data from vehicles, and customer feedback. Moreover, geospatial data plays a pivotal role in understanding route optimization, traffic patterns, and location-based insights.

Additionally, integration with transportation management systems (TMS), freight invoices, and customer databases offers a holistic view of the transportation landscape. By harnessing this diverse range of data types, organizations can derive actionable insights, drive strategic initiatives, and maintain competitiveness in the ever-evolving transportation industry landscape. 

Data Collection and Data Analysis Techniques

Data collection methods and technologies in transportation analytics encompass a diverse range of tools and techniques aimed at gathering comprehensive datasets for analysis. Traditional methods include manual data collection through surveys, interviews, and observations, while modern technologies such as GPS trackers, sensors, and IoT devices offer real-time data capture capabilities. Additionally, transportation management systems (TMS), traffic cameras, and satellite imagery technology provide valuable insights into traffic flow, vehicle movements, and infrastructure utilization. Furthermore, emerging technologies like drones and mobile apps enable crowdsourced data collection, enhancing the breadth and depth of transportation datasets. By employing a combination of these methods and technologies, organizations can capture accurate, timely, and granular data to inform decision-making and drive operational improvements in transportation systems. 

On the other hand, Data analysis techniques and tools play a crucial role in extracting actionable insights from transportation datasets, enabling organizations to optimize routes, enhance operational efficiency, and improve overall performance. Statistical analysis methods such as regression analysis, clustering, and trend analysis allow for the identification of patterns, correlations, and anomalies within transportation data. Moreover, machine learning algorithms and predictive analytics models enable organizations to forecast demand, optimize resource allocation, and mitigate risks proactively. Additionally, data visualization tools such as dashboards, heat maps, and geographic information systems (GIS) facilitate the interpretation and communication of complex transportation data to stakeholders. By leveraging these analysis techniques and tools, organizations can unlock valuable insights, drive informed decision-making, and achieve strategic objectives in transportation management. 

What is the impact of transport analysis?

Transport analytics has a profound impact across various domains, leveraging location intelligence, machine learning, and predictive analytics to optimize transport flow, enhance supply chain efficiency, improve road safety, and enable more efficient strategies. Here’s a breakdown of its impact across different areas:

1. Transport Flow Analysis:  

This tool empowers organizations to analyze and optimize vehicle and foot traffic patterns, leading to improved route planning and reduced congestion. By leveraging location intelligence and predictive analytics, companies can enhance transport flow efficiency, minimize travel times, and ultimately improve overall operational performance. This optimization not only enhances customer satisfaction but also enables organizations to meet increasing demands and stay competitive in dynamic market environments. 

2. Supply Chain Analysis:  

Through transportation analytics, organizations gain valuable insights into supply chain dynamics, facilitating more efficient inventory management and logistics operations. By leveraging machine learning and predictive analytics, companies can optimize routes, streamline distribution processes, and enhance competitive intelligence. This enables organizations to improve supply chain resilience, minimize costs, and gain a strategic advantage in the marketplace. 

3. Road Safety Management:  

This tool plays a crucial role in road safety management by leveraging predictive analytics to identify high-risk areas and implement targeted interventions. By analyzing data from various sources such as weather reports and satellite imagery, authorities can proactively address safety concerns, reduce accidents’ rates, and improve overall road safety. This not only saves lives but also enhances public trust and confidence in transportation systems. 

4. Automatic Scheduling and Planning:  

Integration of machine learning algorithms into transportation analytics systems enables automatic scheduling and planning, optimizing transport routes and resource allocation. By leveraging advanced algorithms, companies can improve operational efficiency, minimize delays, and enhance service reliability. This automation streamlines processes, reduces manual effort, and enables organizations to adapt quickly to changing market conditions, ultimately driving cost savings and improving customer satisfaction. 

5. Traffic Congestion Management:  

This tool aids in traffic congestion management by utilizing predictive analytics and real-time data analysis to identify congestion hotspots and suggest alternative routes. By implementing measures such as high occupancy toll lanes, organizations can alleviate congestion, improve traffic flow, and enhance overall transportation efficiency. This not only reduces travel times and fuel consumption but also enhances air quality and mitigates environmental impact, contributing to sustainable urban development. 

In summary, this tool has a significant impact on various aspects of transportation and logistics, ranging from optimizing transport flow and enhancing supply chain efficiency to improving road safety and managing traffic congestion. By leveraging advanced analytics techniques and integrating data from multiple sources, transportation analytics enables organizations to make data-driven decisions, improve operational efficiency, and enhance overall transportation systems.

Applications of Transportation Data Analytics in Urban Planning 

In the domain of urban planning, transportation data analytics serves as a linchpin for informed decision-making, sustainable development, and enhanced quality of life for residents. By harnessing advanced analytics techniques and leveraging vast datasets, urban planners can gain invaluable insights into traffic patterns, commuter behavior, and infrastructure utilization. This enables them to optimize transportation networks, improve traffic flow, and alleviate congestion hotspots, ultimately leading to more efficient mobility solutions and reduced carbon emissions.  

Additionally, transportation data analytics facilitates evidence-based policy formulation, enabling cities to prioritize investments in public transportation, cycling infrastructure, and pedestrian-friendly urban spaces. By embracing data-driven approaches, urban planners can create smarter, more resilient cities that cater to the evolving needs of residents while fostering economic growth and environmental sustainability. 

What is the role of predictive analytics in cpg?

Predictive analytics plays a crucial role in the Consumer-Packaged Goods (CPG) industry by forecasting consumer demand, optimizing inventory levels, and enhancing supply chain efficiency. By analyzing historical sales data, market trends, and external factors, predictive analytics helps CPG companies anticipate future demand patterns, leading to more accurate production planning and inventory management. This ensures that companies can meet consumer demand effectively while minimizing excess inventory and associated costs, ultimately improving profitability and customer satisfaction.

Implementation of multimodal transport segment-wise analysis

In today’s complex transportation landscape, implementing multimodal transport segment-wise analysis is imperative for optimizing operational efficiency, enhancing customer satisfaction, and driving competitive advantage. By breaking down the multimodal transport system into distinct segments and analyzing each segment’s performance, organizations can uncover valuable insights to inform strategic decision-making and operational improvements. 

1. Data Collection: 

Gathering relevant data is the first step in segment-wise analysis of multimodal transport systems. This includes collecting information on transport volumes, costs, transit times, service levels, and customer preferences across different segments. By capturing comprehensive data sets, organizations can gain a holistic understanding of each segment’s dynamics and identify areas for improvement. 

2. Data Integration: 

Integrating data from various sources and modes of transport is essential for creating a unified dataset for analysis. This involves consolidating data from transportation management systems, GPS trackers, freight invoices, customer databases, and other relevant sources. By centralizing data, organizations can eliminate silos, enhance data quality, and enable more accurate analysis of segment-wise performance. 

3. Segmentation Analysis: 

Applying segmentation techniques to analyze the data allows organizations to identify patterns, trends, and differences across different segments. Using clustering analysis, regression analysis, or other statistical methods, companies can effectively segment the data and uncover insights into each segment’s performance drivers. This analysis provides a foundation for informed decision-making and targeted improvements. 

4. Identify Opportunities and Challenges: 

Utilizing insights gained from segment-wise analysis, organizations can identify opportunities for improvement and address challenges specific to each segment. Whether it’s optimizing routes, adjusting pricing strategies, or improving service levels, understanding segment dynamics enables organizations to tailor their strategies and initiatives for maximum impact. This proactive approach ensures that resources are allocated effectively, and operational efficiency is maximized across the entire transport network. 

5. Decision Making: 

Using the analysis results, organizations can make informed decisions regarding resource allocation, capacity planning, route optimization, pricing strategies, and overall operational improvements. By leveraging data-driven insights, companies can prioritize initiatives that offer the greatest potential for impact and ensure alignment with strategic objectives. This approach empowers organizations to drive continuous improvement and maintain a competitive edge in the dynamic transportation industry landscape. 

Get started with your complimentary trial today and delve into our platform without any obligations. Explore our wide range of customized, consumption driven analytical solutions services built across the analytical maturity levels. 

Future Trends and Challenges in Transportation Data Analytics 

As the transportation industry continues to evolve, future trends and challenges in transportation data analytics are poised to reshape the landscape. Emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) are expected to drive innovation, enabling more sophisticated data collection, analysis, and decision-making processes.  

Predictive analytics and real-time data processing will become increasingly prevalent, empowering organizations to anticipate traffic patterns, optimize routes, and enhance operational efficiency. However, with these advancements come challenges such as data privacy concerns, cybersecurity risks, and the need for skilled talent to manage and interpret complex datasets. 

 Additionally, interoperability issues and data standardization across different modes of transportation pose obstacles to seamless integration and collaboration. Despite these challenges, the potential benefits of transportation data analytics are vast, offering opportunities for organizations to improve safety, reduce congestion, and create more sustainable transportation systems for the future. 

Conclusion:

In conclusion, transportation data analysis is not just a tool but a catalyst for transformative change in the transportation industry. By harnessing the power of data analytics, organizations can optimize routes, enhance operational efficiency, and improve overall transportation systems’ safety and sustainability. As technology continues to evolve and data becomes increasingly abundant, the potential for innovation and improvement in transportation data analysis is boundless. Embracing data-driven decision-making will be key to navigating the challenges and seizing the opportunities that lie ahead, ensuring a smarter, more efficient, and more resilient transportation landscape for the future. 

Frequently Asked Questions

1. how is data analytics used in transportation.

Data analytics is used in transportation to analyze various aspects of the system including route optimization, demand forecasting, vehicle maintenance scheduling, traffic management, and supply chain optimization. By analyzing large volumes of data from sources such as GPS, sensors, traffic cameras, and historical transportation records, data analytics enables transportation companies and authorities to make data-driven decisions, improve operational efficiency, reduce costs, and enhance overall service quality.

2. Why is transport analysis important?

Transport analysis is crucial for several reasons: – It helps identify inefficiencies and bottlenecks in transportation systems, enabling optimization and improvement. – It aids in forecasting future transportation needs and demands, allowing for better planning and resource allocation. – It assists in identifying safety hazards and risks, leading to improved road safety measures. – It enables transportation companies to optimize routes, reduce fuel consumption, and minimize environmental impact. – It supports policy-making and infrastructure development by providing insights into transportation trends and patterns.

3. What is the transportation problem in analytics?

The transportation problem in analytics refers to the classic optimization problems in which goods must be transported from a set of sources to a set of destinations while minimizing total transportation costs. These problems are characterized by constraints such as capacities at sources and demands at destinations. The goal is to determine the optimal allocation of goods to transportation routes to meet demand while minimizing costs.

4. What are the methods of data collection in transportation?

There are several methods of data collection in transportation, including: – GPS tracking: Utilizing GPS devices installed in vehicles to track their locations and movements in real-time. – Traffic sensors: Installing sensors along roadways to collect data on vehicle speeds, volumes, and congestion levels. – Traffic cameras: Using cameras to monitor traffic flow, detect incidents, and gather visual data for analysis. – Surveys: Conducting surveys of travelers to gather information on travel behavior, preferences, and patterns. – Electronic toll collection systems: Collecting data on vehicle movements and usage through toll collection systems. – Transport management systems: Utilizing software systems to collect and analyze data on transportation operations, including fleet management, scheduling, and routing.

5. How can transportation data analytics improve efficiency?

Transportation data analytics improves efficiency by providing insights into traffic patterns, route optimization, and resource allocation. By analyzing large datasets, organizations can identify bottlenecks, streamline operations, and reduce transit times, ultimately enhancing overall efficiency and cost-effectiveness.

6. What is the role of artificial intelligence in transportation data analytics?

Artificial intelligence plays a crucial role in transportation data analytics by enabling predictive modeling, real-time decision-making, and automation of tasks. AI algorithms can analyze complex data sets, identify patterns, and predict future trends, allowing organizations to proactively address issues and optimize transportation systems for maximum efficiency.

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Home > Centers > TREC > Seminar Series > 44

PSU Transportation Seminars

Using "Big Data" for Transportation Analysis: A Case Study of the LA Metro Expo Line

Mohja L. Rhoads , South Bay Cities Council of Governments

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Description.

Access to a comprehensive historical archive of real-time, multi-modal multi-agency transportation system data has provided a unique opportunity to demonstrate how “big data” can be used for policy analysis, and to offer new insights for planning scholarship and practice. We illustrate with a case study of a new rail transit line. We use transit, freeway, and arterial data of high spatial and temporal resolution to examine transportation system performance impacts of the Exposition (Expo) light rail line (Phase 1) in Los Angeles. Using a quasi-experimental research design, we explore whether the Expo Line has had a significant impact on transit ridership, freeway traffic, and arterial traffic within the corridor it serves. Our results suggest a net increase in transit ridership, but few effects on traffic system performance. Given the latent travel demand in this heavily congested corridor, results are consistent with expectations. The benefits of rail transit investments are in increasing transit accessibility and person throughput within high-demand corridors; effects on roadway traffic are small and localized.

Biographical Information

Mohja Rhoads received her PhD from the University of Southern California (USC) in Policy, Planning and Development. She received her Masters from the University of California at Los Angeles (UCLA) in Planning and a Bachelors in Economics and French from Columbia University. Her research interests include the nexus between Information Communication Technologies (ICTs) and Transportation, namely the ability of ICTs to enable flexible workplace practices and the resulting travel implications. Her other research interests include using Big Data to answer old transportation questions in new ways, and Transportation and Land Use in a neighborhood context. Mohja is currently a Senior Research Associate for the South Bay Cities Council of Governments and a Research Fellow at the Price School of Public Policy at USC. In her spare time, Mohja likes playing the Ukulele, singing Jazz and reading printed books on all subjects.

Local transit -- Decision making -- Statistical methods, Transportation -- Planning -- California -- Los Angeles, Data mining, Street railroads -- California -- Los Angeles

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Rhoads, Mohja L., "Using "Big Data" for Transportation Analysis: A Case Study of the LA Metro Expo Line" (2014). PSU Transportation Seminars . 44. http://archives.pdx.edu/ds/psu/18294

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King County Metro (Seattle, WA), North Link project

Los angeles county metropolitan transportation authority (los angeles, ca), nextgen bus plan, massachusetts bay transportation authority & massachusetts department of transportation (boston, ma), better bus project & public engagement plan, metro transit (madison, wi), racial equity and social justice initiative, metro transit (minneapolis-st. paul, mn), better bus stops, oakland department of transportation (oakland, ca), strategic plan, slow streets & master bike plan, san francisco county transportation authority (san francisco, ca), district 10 mobility management study, san francisco municipal transportation agency (san francisco, ca), service equity strategy , seattle department of transportation (seattle, wa), transportation equity workgroup & transportation equity team, transit alliance (miami, fl), advocate-led bus network redesign, transitmatters (boston, ma), advocating for nighttime transit service, transportation choices coalition (seattle, wa), sound transit and city of seattle rainier valley transit oriented development , trimet (portland, or), equity index & transit equity advisory committee.

Interviewee: Maha Jahshan, Public Engagement Planner

Sound Transit in the Seattle region set out to construct three new Link light-rail stations in the northern part of the city by 2021. Changes to the King County Metro bus network and increased access to alternative modes of transit will accompany the construction of these stations. The bus network changes include 40 bus routes which will streamline and directly serve the stations of the North Link project . 

The North Link project is especially important for building north to south connections for residents of Seattle and for local east to west connections across town. Increases in development and gentrification have displaced people outside of the center of the City, forcing many Black, Indigenous, and people of color communities to move to areas with poor transit access. The project area remains a large transit hub as many hospitals, universities, and businesses employ BIPOC communities who rely on transit to get to their place of employment or access services.  

  Outreach and engagement for the project included translating information to seven languages, giving technical briefings, assembling ethnic media and social media, hosting in-language meetings with community groups, and compensating community-based organizations (CBOs) to conduct focus groups and engagement. In addition, a Mobility Board made up of diverse community members helped co-create the service change proposal with the transit agency project team and a Partner Review Board made up of institutions, large CBOs, and partner agencies also reviewed the proposal. The project is a collaboration between several agencies including King County Metro, Seattle DOT, Sound Transit, Community Transit, WashDOT.

Interviewee: Conan Cheung, Senior Executive Officer, Service Planning, Scheduling & Analysis

NextGen is LA Metro’s bus network redesign, led by Metro’s service planning department. NextGen began development in 2018 and was adopted in 2020, with a service plan that emphasizes a grid of frequent routes. NextGen exemplifies how new kinds of quantitative data can inform service planning. The NextGen service plan would increase walking access to frequent transit service to 83% of Metro’s bus riders (who have an average household income of less than $18,000) compared to 48% pre-pandemic.

NextGen was informed by cell-phone location-based services (LBS) data and farecard “tap” data. By comparing LBS and farecard data, Metro could see how transit competed with other transportation modes for common trips. These data explain where people were using or not using transit, but not why they made those travel choices. Metro also ran an online survey to get information from current riders, former riders, and nonriders to identify barriers to using the bus system. The agency created an external working group with 60 different organizations including faith-based groups, Councils of Government, and advocacy organizations, and hosted 400 public workshops, stakeholder meetings, and community events.

The use of LBS data gave Metro insights that weren’t present from previous data sources like the Census and National Household Travel Survey data – for example, that travel intensity doesn’t align completely with employment or residential density. LBS underscored the importance of regional destinations, and captured short trips that are typically underreported in household travel diaries. Both LBS and the survey data showed the importance of frequency to making transit competitive for these short trips.

LA Metro developed a “transit equity score” to define “transit equity focused areas” with the greatest need. The score includes the density of zero-car households, households with low incomes, students between the age of 10-19, people over the age of 55, single mothers, people with disabilities, and people of color.

While LA Metro has committed to implementing NextGen in its entirety, in fiscal year 2021 the agency adopted a budget that reduced bus service by 20% compared to pre-pandemic service levels. It’s unclear how NextGen will be implemented in this context. The stakeholder working group that helped inform the NextGen plan did not review the Metro budget, limiting the input from these “equity committees” that are project-specific. 

More broadly, the progress of equity initiatives at Metro demonstrate the challenges that can arise from agency structure. In addition to operating transit, Metro includes a capital construction division that builds both transit and highway projects, and a regional planning/programming division that distributes transportation sales tax revenue to dozens of municipal governments. There is not always consistency in principles, priorities, goals, and stakeholders across these functions. For example, the agency’s long-range transportation plan also identifies equity neighborhoods, but with different criteria than those used in NextGen. Metro recently hired a Director of Race and Equity with the goal of better coordinating these efforts.

Interviewees: Anna Gartsman, Director of Strategic Research, MBTA; Laurel Paget-Seekins, former Assistant General Manager for Policy, MBTA; Anthony Thomas, Manager of Policy Development & Outreach, MBTA

The Massachusetts Bay Transportation Authority has become a leader in generating and using data for transit reform initiatives, including plans to redesign the bus network to match regional needs through its Better Bus Project . This success derives from building the internal capacity to do high-quality analysis at the MBTA and at the Massachusetts Department of Transportation’s Office of Performance Management and Innovation (OPMI), and from creating analytics departments tasked with high-level, cross-cutting research.  

The interviewees pointed out that many limitations of quantitative analysis come from attempting to use poorly-suited data to answer equity questions. Currently, most transit equity analysis uses data on where people live, not where they travel; this obscures trips made by riders with low incomes between high-income areas, for example. 

Instead, it’s important to look beyond commonly-used data sources for more suitable data. For example, MassDOT used data from Streetlight, a company that analyzes location-based services (LBS) data from cell phones, to inform the bus network redesign. Because LBS data includes travel across different modes, it helps show potential unmet demand for transit service. The use of LBS data allowed the agency to identify popular destinations as well as trips where transit service was competitive with driving. Their analysis suggested that respondents tended to have a lower or higher willingness to use transit (and tolerate everything that comes with it), rather than having strong feelings about trade-offs between different aspects of transit quality (i.e. frequency vs. walking distance).

The agency “groundtruthed” these findings using surveys and open house meetings (including virtual meetings during the pandemic). It also organized an external working group with 37 members representing municipalities in the MBTA service area, transportation advocates, business organizations, environmental justice and community-based organizations, and elected officials. The working group initially met monthly and as of December 2020 meets quarterly to review analysis and provide input on how the agency communicates the benefits of the redesign.

The interviewees emphasized the importance of doing outreach and qualitative research to understand and accommodate “edge cases:” people with non-standard commutes or preferences for transit, who will be excluded from transit policy solely that caters to the standard rider. One example of the role of qualitative outreach was fare vending: the MBTA held focus groups with seniors who reported a preference for retail locations over vending machines. Even though retail locations were not available 24/7, they offered a greater feeling of safety than vending machines. More engagement with seniors will be necessary because they are underrepresented in location-based datasets.

The interviewees found value in the structure of OPMI, which sits within MassDOT and can access data from other transportation agencies. Because it exists outside of operating divisions (like Customer Experience) it has the space to develop and execute research projects aimed at predicting future needs. For example, during the pandemic OPMI has developed an “employer panel survey,” a group of major employers who are repeatedly surveyed so that OPMI can understand when and how employees may return to on-site work.

Interviewee: Ann Schroeder, Assistant to the General Manager

Metro Transit, the public transportation operator for Madison, Wisconsin, participates in the City of Madison’s Racial Equity and Social Justice Initiative (RESJI). The goals of the RESJI are to account for diverse perspectives and experiences in city policies and practices and to achieve equitable outcomes for all Madison residents. The RESJI began in 2013 as a city-wide effort to be institutionalized across departments and withstand electoral turnover. City of Madison staff take on RESJI work on a volunteer-basis (in addition to responsibilities for their primary role). 

At Metro Transit, much of the RESJI work centers on transforming hiring practices in order to achieve greater diversity throughout its workforce. Metro Transit seeks to break down barriers to entry for groups historically excluded from public transit roles, like women and people without high school or college degrees, and to promote the advancement of frontline workers, a large share of whom are BIPOC. An equitable hiring team, formed as part of the RESJI, developed and regularly updates a plan to review and adjust hiring practices. So far, the agency changed the status of new frontline employees from part-time to full-time to create greater stability and increase pay for its frontline workers, and it diversified its interview panels to make hiring decisions more inclusive and collaborative.

The RESJI has also spurred the formation of a racial equity team at Metro Transit. The racial equity team has faced some challenges in recruiting and establishing a core team  due to a significant bus operator shortage that limits the capacity of operators to volunteer their efforts. Transforming agency culture across the board to prioritize equity is another challenge Metro Transit has faced in its implementation of the RESJI. 

Interviewees: Berry Farrington, Senior Planner; Anna Flintoft, Urban Design Manager; Cyndi Harper, Manager of Route Planning 

As Metro Transit, the transit operator for the Minneapolis-St. Paul region, was planning the Green Line light-rail project, advocates and elected officials argued that the agency was focused on transit extensions into the suburbs while neglecting the bus experience in urban areas. In response, Metro Transit agreed to build additional bus shelters in racially concentrated areas of poverty, calling it the Better Bus Stops program. Metro Transit’s outreach approach for this program exemplifies community process that changed agency decisions.

Metro Transit secured a $3.26 million federal grant for the bus stops work through the US DOT’s Ladders of Opportunity program in 2014. Ten percent of the grant budget went to community engagement . Metro Transit contracted with one entity, a “Community Engagement Team” (made up of two citywide nonprofits and a program of the University of Minnesota). The CET then subgranted to 11 community-based organizations (CBOs) who led engagement in different neighborhoods; the selections were made by a committee of community leaders. 

This approach simplified procurement and relationships for Metro Transit, which worked directly with large citywide nonprofits with which it had established relationships. To quote Metro Transit’s own report on the project: “As an intermediary, the CET offered relationships, trust, and access to community partners that otherwise would not have been available to Metro Transit to create a deeper pool of subcontract applicants.”

The CBOs conducted outreach through a range of activities, including tabling at community events, door-knocking, and focus groups. Riders were asked for their thoughts on shelter placement, design, features, community assets of historic significance, and how Metro Transit could advance regional equity. As a result of CBO involvement, Metro Transit was able to get feedback from a sample representative of bus riders; the demographics of the survey results were similar to the demographics of bus riders in terms of age, gender, Hispanic ethnicity, and race. Twenty percent of respondents reported having a disability, and 57% of survey respondents heard about the survey through a CBO.

Interviewees reported that hearing directly from riders “changed the conversation at Metro Transit” and revealed to the agency how important certain amenities, including seating and shelters, were to riders. As a result of the project, Metro Transit changed its standards for bus shelter siting. Before Better Bus Stops, 40 boardings/day were needed to justify a shelter in Minneapolis or St. Paul, compared to 25/day in the suburbs, where service frequency was lower and riders were assumed to wait longer for the bus. However, riders engaged in the project argued that this was unfair. The agency’s Department of Strategic Initiatives also analyzed data from a University of Minnesota study and found no evidence of longer wait times in suburban areas.

Engagement also changed the agency’s approach to shelter design. Before the project, the agency’s philosophy was that any stop that qualified for a shelter should receive the largest shelter possible. Riders with disabilities pointed out that Metro Transit frequently placed large shelters on narrow sidewalks that made it difficult to pass. As a result, the agency developed a narrow shelter design. The agency also developed a new internal process for shelter placement, circulating design drawings of proposed shelters through the bus operations department for feedback.

Since the conclusion of the federal grant, the project has evolved into a program for the agency’s full service area. As of January 2020, the agency has added 135 shelters and upgraded another 78 with light or heat. (There are about 950 shelters in the system.) In racially concentrated areas of poverty, roughly two-thirds of boardings now take place at stops with shelters — similar to the results across the system. 

Interviewee: Ryan Russo, Director of Oakland DOT

When the Oakland Department of Transportation was created in 2016, its founding strategic plan outlined equity as a pillar to drive the agency’s work. Since then OakDOT has worked closely with Oakland’s Department of Race and Equity to increase staff capacity to identify, understand and address structural racism in transportation through professional development and skills training; to conduct equitable community engagement; and to use data as a tool for equity. At OakDOT, equity, diversity and inclusion (EDI) competency is treated as a professional skill on par with technical skill development, requiring training and ongoing development. Receiving training to hone competencies that advance equity work are integrated in staff work plans and rewarded through salary bumps and certification pay. Much of OakDOT’s EDI work emphasizes the need for greater recruitment and retention of BIPOC to have more equitable representation across the agency, including at upper management levels. 

OakDOT’s recent update to its master Bicycle Plan has been cited as transit equity in action due to the agency’s engagement with community partners to reach marginalized communities, whose participation helped change the narrative around biking while Black and Brown in Oakland. For their community engagement approach, OakDOT met with people in spaces familiar to them (holding 25 mobile workshops) and used new strategies like design charrettes to obtain feedback, rather than solely in traditional public outreach hearings. The agency also used survey data and qualitative data collected with the help of five trusted community-based cycling organizations with Black and Brown membership and leadership. The dynamic, intertwined use of quantitative and qualitative data helped illuminate racial disparities related to police department cycling stops, which was a key finding of the bike plan. 

Interviewees: Rachel Hiatt, Assistant Deputy Director for Planning, and Paige Miller, Senior Communications Officer, SFCTA; Ezra Kong, Managing Partner and Co-Founder, Reflex Design Collective; Hana Creger, Environmental Equity Program Manager, Greenlining Institute

Some of the most innovative work in transit equity occurs when transit agencies work in close collaboration with nonprofit organizations. The San Francisco County Transportation Authority (SFCTA) applied the Mobility Equity Framework , developed by the Greenlining Institute, to implement equitable mobility improvements in its District 10 in southeast San Francisco.

Reflex Design Collective, collaborating with community-based organizations (CBOs) as subcontractors, led a co-design process to engage residents, who were compensated for participating, from the start of the initiative. The agency also committed to weigh qualitative information as much as quantitative analysis, which represented a shift in culture. SFCTA had to counteract residents’ deep distrust of the government to productively collaborate with them. These actions were critical to building back that trust, demonstrating that SFCTA valued community partnership and respected their lived expertise alongside traditional technical expertise. 

The approach sought out and actively engaged community residents, particularly those most impacted by transit changes such as communities of color and non-English speakers, in co-designing solutions to improve mobility in District 10. The engagement of residents broadened the focus to address existing disparities in air quality and access to economic opportunity and to build community involvement in decision-making overall.

According to interviewees, this effort was made possible by widespread awareness about equity and displacement across the region, including on the SFCTA Board. The approach with District 10 demonstrated the value of an equity-centered approach and caused SFCTA to view the Mobility Equity Framework very favorably. The agency has revised its communications and outreach guidelines to reflect the Framework, and it is applying it within a larger-scale congestion pricing project currently. SFCTA has also begun to consider budgeting for CBO involvement and participant compensation in all of its projects, although these are not yet standard practices.

Interviewee: Sandra Padilla, former Lead on Service Planning

The Muni Service Equity Strategy started in 2016 and is a continuing effort to improve transit service in nine neighborhoods in San Francisco, California. The nine neighborhoods, identified as equity neighborhoods, have shares above citywide averages of people of color, low car ownership, or people with low incomes. The strategy set out to determine critical transit needs in these neighborhoods with high transit reliance and where solutions could be successfully implemented and quickly achieved. 

The Muni Service Equity Policy was created in collaboration with the Muni Service Equity Working Group (which SFMTA formed in 2014 and includes government agencies and community-based organizations, or CBOs). The policy mandates a biennial strategy to identify projects that will make service equal or better in equity neighborhoods. In recent years outreach for the two-year strategy included working with CBOs in each neighborhood, multilingual presentations and materials, and meetings held in neighborhood spaces with snacks and childcare.

The strategy is updated every two years and presented to the SFMTA board, ahead of the biennial budget process. The process of updating the strategy every two years allows for communities to experience rapid changes in service, long-term planning and community input, and an opportunity to influence the SFMTA’s budget.

SFMTA used a Caltrans grant and funding from Proposition K, a local sales tax administered by the SF County Transportation Authority, to fund the community outreach efforts for the program in 2017 and 2018.

Interviewees: Annya Pintak, Manager of Transportation Equity & Laura Lee Sturm, Transportation Access Program Manager 

In 2019, Seattle DOT created two workgroups, to engage community leaders outside of the agency and to engage agency staff. Coordination between the external and internal teams is facilitated by the Manager of Transportation Equity who serves as a liaison advocating both internally and externally. 

The external group, the Transportation Equity Workgroup (TEW), consist of ten BIPOC community members with personal and professional affiliations to community-based organizations in Seattle. This external workgroup was created to provide specific recommendations and set goals for SDOT’s Transportation Equity Agenda. Members of the TEW were compensated with a $5,000 yearly stipend. 

SDOTs internal team, the Transportation Equity Team, consists of 20 agency staff from various departments. The internal team works simultaneously with the external team, providing feedback and recommendations set forth by the TEW on the equity agenda.

Interviewees: Azhar Chougle, former Executive Director

Miami-Dade County’s bus network redesign (which began in May 2019 and was adopted in October 2020) was an unusual collaboration between Miami-Dade Transit (MDT) and the nonprofit Transit Alliance . Transit Alliance was hired by Miami-Dade county government to lead the redesign, subcontracting Jarrett Walker + Associates to lead technical analysis. Transit Alliance’s approach to public engagement and the use of qualitative information gained from that engagement stand out as lessons that can be applied by other agencies, whether they hire community-based organizations to conduct outreach or rely on in-house capacity. 

The network redesign was prompted by the poor reach of the previous bus network. Only 10% of county residents had access to frequent transit. The final plan emphasizes a grid of frequent bus routes, doubling the number of residents within a 5-minute walk to a bus arriving at least every 15 minutes. The proportion of Black county residents within walking distance of frequent transit will go from 9% to 31%. 

Transit Alliance’s approach to building public understanding and buy-in to the network redesign was cognizant of how an earlier MDT attempt to redesign its network had fallen short. The group took a narrative-driven approach that could explain to media, decisionmakers, and riders that the bus system was failing and specific kinds of change would be necessary to build ridership and better serve Miami. The transit agency was initially resistant to an approach that admitted past failures, but County Mayor Carlos Gimenez was strongly supportive.

Transit Alliance focused public engagement in communities of color, where there was historic mistrust of county government. According to Transit Alliance, three months of sustained engagement–primarily listening–were necessary to build trust in these areas. Government’s historic tendency when confronted by community mistrust is to “retreat and move to friendly ground; [instead] we double down and do more” engagement, Chougle said. The information gained through this engagement was used to inform the network redesign. According to Chougle, “every time a decision was being made solely by data, [we] interrupted” to bring in knowledge learned through engagement. Instead of “data-driven” decisions, Transit Alliance says, design should be “data-informed.”

The consultant JW+A developed access-to-opportunity and “access to frequent transit” measures that Transit Alliance used to demonstrate the inequity of existing transit and the possibilities of a redesigned network. This was important because elected officials recognized that there were inequities in the system, but discussion of solutions tended to focus on long-term capital projects. Access-to-opportunity metrics were a powerful way to drive the conversation towards improvements in existing service.

Transit Alliance demonstrated project leadership skills that are a good model for any transit agency leading an equity initiative. When elected officials or other stakeholders challenged elements of the network redesign, Transit Alliance staff began by explaining the rationale behind proposals, instead of immediately appeasing them by changing the design. This helped gain the trust of transit agency staff as well.

Interviewee: Jarred Johnson, Executive Director

In 2019 , the MBTA increased bus service during early mornings and late nights by about 140 more trips per week. The improvements resulted from a multi-year, collaborative effort by the MBTA, TransitMatters, and other stakeholders to draw attention to and address needs for nighttime service.

The seed for the service expansion came years earlier from TransitMatters, a nonprofit transit advocacy group in the Boston region. In 2016, TransitMatters was calling on the MBTA to restore and expand late-night service, arguing in Commonwealth Magazine that there was a cost-effective solution to meet the needs of overnight riders. Their “NightBus” concept proposed that the MBTA could add trips every 75 minutes, all night long, on eight existing high-ridership early-morning bus routes. The routes ran through most EJ communities in and around Boston before converging on Copley Square, a central location.

Though MBTA planners were initially unsure of the proposal’s feasibility, TransitMatters’ broader demand caught the attention of the MBTA’s Fiscal Management and Control Board . The FMCB directed MBTA staff to research, then respond to, riders’ needs for nighttime service — starting by understanding and building out TransitMatters’ NightBus proposal.

Collaborating with city partners and TransitMatters, the MBTA designed and distributed a survey to ask the public about their overnight travel needs. The survey revealed that low-income workers could benefit from additional service during late-night and early morning periods. Staff from the Cities of Boston and Cambridge engaged directly with employers in their jurisdictions to identify the needs of overnight commuters. The MBTA also analyzed a mix of origin-destination data from multiple sources to get a clear picture of demand patterns throughout the night.

Following these research and outreach efforts, the MBTA drafted a pilot proposal for expanded service, with input from community partners including TransitMatters. In 2018, the FMCB approved pilots that extended bus service later in the night and earlier in the morning. Service improvements included increasing frequency and lengthening span of service on certain early-morning and late-night bus routes, as well as a new overnight route (which strung together other existing routes).

By 2019, the early morning and late-night routes proved to be popular with riders. Responding to the demand that riders demonstrated, the MBTA made 140 weekly trips during those periods permanent.

The overnight service was not continued: low ridership during the pilot had resulted from poor branding and signage, confusing routing that made transfers difficult, and a lack of consideration of safety concerns that riders, particularly women, had with overnight travel. Underestimating the latter outcome led TransitMatters to recognize its own lack of gender diversity, which the organization has worked to address since.

Each party played an important role in ultimately improving nighttime travel for Boston transit riders. TransitMatters originated the concept, then cultivated the political will and maintained pressure for the change. At the MBTA, the FMCB prioritized the demand; staff responded with thorough outreach and analysis to reinforce their service proposals. The cities of Boston and Cambridge supported outreach to ensure that the needs of their constituents were addressed.

Interviewee: Hester Serebrin, Policy Director, Transportation Choices Coalition

In 2018, the Sound Transit Board created an initiative to facilitate the construction of affordable housing on surplus land, originally acquired for construction of light-rail routes in Rainier Valley in Seattle, Washington. The initiative was a collaboration between Sound Transit and the Seattle Housing Authority. 

An extensive outreach process for the project set out to determine four things: high-priority sites for construction, uses for commercial/community spaces, density preferences on the sites, and general community support for affordable housing. The outreach included a survey that yielded 945 responses and in-person engagement (six meetings with roughly 100 community members, with an emphasis on limited-English speakers and historically underrepresented groups), conducted via a contract with Puget Sound Sage, a community-based organization. In-person engagement was more successful than the survey at capturing the needs of commonly marginalized groups in Seattle. The in-person outreach found preferences for larger apartments to accommodate bigger families, larger sites for construction, cultural services for the commercial/community spaces, and zero-interest loans. 

The Memorandum of Understanding between the two agencies included key priorities determined via the public outreach process and in the fall of 2020 the Sound Transit Board adopted a resolution allowing Sound Transit to transfer the sites to the City of Seattle at no cost for the development of affordable housing.  

Interviewees: Carl Green, Jr., former Title VI and Equity Programs Administrator; Roberto Guttierez, Senior Project Coordinator (Transit Equity, Inclusion, and Community Affairs Department); Scott Nance, Senior Communications Specialist.

TriMet’s approach to institutionalizing equity is notable for its use of standing external committees, leveraging its Civil Rights Title VI program , development of new metrics and an equity index that measure different aspects of service provision, and the growth of internal capacity to do equity work.

In 2013, TriMet began the development of a quantitative “Equity Index.” The index identifies “equity neighborhoods” by weighing ten factors: people of color, limited English proficiency, youth population, limited vehicle access, affordable housing units, low-income population (200% of federal poverty level), senior population, people with disabilities, low- and medium-wage jobs, and key retail and human/social service destinations.

In 2018, Oregon passed new statewide transportation funding legislation, HB2017, which provides an additional $48 million annually for TriMet; the agency has prioritized service investments in the equity neighborhoods determined by the Index for this additional funding. 

TriMet incorporated equity metrics into its 5-year business plan process. In 2017, the agency found that the average age of vehicles on routes serving mostly people of color or people with low incomes was 12 percent older than the average age of the vehicles on the other routes. As a result, the agency retired older buses and examined how it had been assigning vehicles to different depots, and it changed its process to improve this metric in future years.

Objective: Ensure equitable distribution of services and resources across the TriMet system

Measure: Minority and low-income access within five percent or better than non-minority and non-low-income access across different measures:

•  Revenue hours provided
•  Vehicle loads
•  On time performance
•  Service availability
•  Vehicle assignment
•  Stop amenities

Central to TriMet’s equity work is the use of its Transit Equity Advisory Committee , which includes a TriMet board member and representatives from 16 organizations working with transit-reliant populations, youth, community colleges, housing, and advocacy groups. The TEAC meets monthly and is primarily a way for TriMet to brief and get input from community partners on agency projects, initiatives, and research studies that could influence the equitable provision of service. In 2020, for example, TEAC agendas included discussion of changes to the low-income fare enrollment process, proposed bus lane and light-rail extension projects, and changes to transit policing.

Many public agencies have advisory committees that accept input but fail to change agency decision-making. TriMet interviewees say the TEAC stands out because agency leadership sees it as a serious venue to vet draft policies and inform policy conversations. In other words, proposed changes are discussed in committee early enough that meaningful change can result, and agency leadership views TEAC buy-in as important. The presence of an agency board member on the committee creates a conduit to the rest of the board.

According to the interviewees, TriMet has intentionally grown the number of staff assigned to equity-related work, to 20 full-time staff assigned to equity initiatives across multiple departments.

Vehicle routing Problem for cold chain logistics based on data fusion technology to predict travel time

• Original Paper
• Published: 15 September 2024
• Volume 24 , article number  55 , ( 2024 )

Cite this article

• Qinyang Bai 1 ,
• Yuxiang Yuan 2 ,
• Xueqin Fu 3 &
• Zhili Zhou 1  

Cold chain logistics requires low-temperature transportation, which consumes more energy and has higher distribution costs than ordinary logistics. Moreover, as the scale of cities continues to expand, traffic congestion is becoming more frequent. Therefore, it is particularly important to plan the distribution route reasonably. In this paper, we study the problem of cold chain logistics vehicle path planning based on travel time prediction. First of all, multiple connected routes with real-time changes in traffic conditions between customers in the road network were considered to describe the distribution scene. Second, a genetic algorithm-optimized backpropagation algorithm fused travel time predictions for road segments based on fixed detector technology and floating car technology to improve the accuracy of road segment travel time prediction. Then, based on the prediction of road segment travel time, a method for predicting the travel time of the route is proposed, and the actual road network is transformed into a travel time network for each customer. Finally, the vehicle routing problem in cold chain logistics was investigated using predicted travel time as input. This problem is formulated as a bi-objective model aimed at minimizing costs and carbon emissions. To address this problem, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was proposed. The study provides support for cold chain logistics distribution companies to develop distribution strategies based on local environmental policies and their own operational conditions.

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Akkerman R, Farahani P, Grunow M (2010) Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. OR Spectr 32:863–904

Article   Google Scholar  

Al Theeb N, Smadi HJ, Al-Hawari TH, Aljarrah MH (2020) Optimization of vehicle routing with inventory allocation problems in cold supply chain logistics. Comput Ind Eng 142:106341

Amorim P, Almada-Lobo B (2014) The impact of food perishability issues in the vehicle routing problem. Comput Ind Eng 67:223–233

Bai Q, Yin X, Lim MK, Dong C (2022) Low-carbon vrp for cold chain logistics considering real-time traffic conditions in the road network. Ind Manag Data Syst 122:521–543

Chen J, Liao W, Yu C (2021) Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission. Comput Ind Eng 161:107663

Cheu RL, Lee DH, Xie C (2001) An arterial speed estimation model fusing data from stationary and mobile sensors, In: ITSC 2001, 2001 IEEE intelligent transportation systems, proceedings (Cat. No. 01TH8585). IEEE, pp 573–578

Cheu RL, Lee DH, Xie C (2002) An arterial speed estimation model fusing data from stationary and mobile sensors. In: Intelligent transportation systems, IEEE

Diabat A, Jabbarzadeh A, Khosrojerdi A (2019) A perishable product supply chain network design problem with reliability and disruption considerations. Int J Prod Econ 212:125–138

Gao Z, Zhu J, Huang C, Dong D (2004) A method of travel time survey and prediction. J Transp Inf Saf. https://doi.org/10.3963/j.issn.1674-4861.2004.04.011

Guo J, Wang X, Fan S, Gen M (2017) Forward and reverse logistics network and route planning under the environment of low-carbon emissions: A case study of shanghai fresh food e-commerce enterprises. Comput Ind Eng 106:351–360

Guo X, He J, Yu H, Liu M (2023) Carbon peak simulation and peak pathway analysis for hub-and-spoke container intermodal network. Transp Res Part E Logist Transp Rev 180:103332

Kang K, Han J, Pu W, Ma Y (2019) Optimization research on cold chain distribution routes considering carbon emissions for fresh agricultural products. Comput Eng Appl 55:259–265

Google Scholar  

Kim HW, Joo GH, Lee DH (2019) Multi-period heterogeneous vehicle routing considering carbon emission trading. Int J Sustain Transp 13:340–349

Kim K, Kim H, Kim SK, Jung JY (2016) i-rm: an intelligent risk management framework for context-aware ubiquitous cold chain logistics. Expert Syst Appl 46:463–473

Kumar BA, Vanajakshi L, Subramanian SC (2017) Bus travel time prediction using a time-space discretization approach. Transp Res Part C Emerg Technol 79:308–332

Kumar SV, Chaitanya Dogiparthi K, Vanajakshi L, Subramanian SC (2017) Integration of exponential smoothing with state space formulation for bus travel time and arrival time prediction. Transport 32:358–367

Kuo R, Edbert E, Zulvia FE, Lu SH (2023) Applying NSGA-ii to vehicle routing problem with drones considering makespan and carbon emission. Expert Syst Appl 221:119777

Li N, Li G (2022) Hybrid partheno-genetic algorithm for multi-depot perishable food delivery problem with mixed time windows. Annals of Operations Research , 1–32

Li Y, Lim MK, Tseng ML (2019) A green vehicle routing model based on modified particle swarm optimization for cold chain logistics. Ind Manag Data Syst 119:473–494

Lim MK, Li Y, Wang C, Tseng ML (2022) Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine. Ind Manag Data Syst 122:819–840

Lin W, Wei H, Nian D (2022) Integrated Ann-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm. Expert Syst Appl 209:118319

Liu C, Kou G, Zhou X, Peng Y, Sheng H, Alsaadi FE (2020) Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach. Knowl-Based Syst 188:104813

Ma X, Liu T, Yang P, Jiang R (2016) Vehicle routing optimization model of cold chain logistics based on stochastic demand. J Syst Simul 28:1824

Menares F, Montero E, Paredes-Belmar G, Bronfman A (2023) A bi-objective time-dependent vehicle routing problem with delivery failure probabilities. Comput Ind Eng 185:109601

Ottmar RD (2014) Wildland fire emissions, carbon, and climate: modeling fuel consumption. For Ecol Manag 317:41–50

Rice J, Van Zwet E (2004) A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst 5:200–207

Robinson S (2005) The development and application of an urban link travel time model using data derived from inductive loop detectors. Department of Civil and Environmental Engineering, Imperial College London

Song BD, Ko YD (2016) A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery. J Food Eng 169:61–71

Stellingwerf HM, Groeneveld LH, Laporte G, Kanellopoulos A, Bloemhof JM, Behdani B (2021) The quality-driven vehicle routing problem: Model and application to a case of cooperative logistics. Int J Prod Econ 231:107849

Tang H, Tang H, Zhu X (2021) Research on low-carbon vehicle routing problem based on modified ant colony algorithm. Chin J Manag Sci 29:118–27

Tang Q, Hu X (2020) Modeling individual travel time with back propagation neural network approach for advanced traveler information systems. J Transp Eng Part A Syst 146:04020039

Wang S, Tao F, Shi Y (2018) Optimization of location-routing problem for cold chain logistics considering carbon footprint. Int J Environ Res Public Health 15:86

Wu Q (2015) Travel time estimation and prediction for urban road networks. Zhejiang University, Zhejiang

Wu Y, Ma Z (2017) Time-dependent production-delivery problem with time windows for perishable foods. Syst Eng Theory Pract 37:172–181

Xiao Y, Konak A (2016) The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transp Res Part E Logist Transp Rev 88:146–166

Xing L, Liu W (2021) A data fusion powered bi-directional long short term memory model for predicting multi-lane short term traffic flow. IEEE Trans Intell Transp Syst 23:16810–16819

Zhang S, Gajpal Y, Appadoo S (2018) A meta-heuristic for capacitated green vehicle routing problem. Ann Oper Res 269:753–771

Zhang Z, Wang Y, Chen P, He Z, Yu G (2017) Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns. Transp Res Part C Emerg Technol 85:476–493

Zhao Z, Li X, Zhou X, Liu C (2020) Research on green vehicle routing problem of cold chain distribution: considering traffic congestion. Comput Eng Appl 56:224–231

Zheng F, Pang Y, Xu Y, Liu M (2021) Heuristic algorithms for truck scheduling of cross-docking operations in cold-chain logistics. Int J Prod Res 59:6579–6600

Zheng Y, Xie M, Wang X (2019) Research on passenger flow forecast of hangzhou metro based on lstm-svr, In: 2019 international conference on artificial intelligence and advanced manufacturing (AIAM), IEEE. pp 273–276

Zhu L, Hu D (2019) Study on the vehicle routing problem considering congestion and emission factors. Int J Prod Res 57:6115–6129

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Bai, Q., Yuan, Y., Fu, X. et al. Vehicle routing Problem for cold chain logistics based on data fusion technology to predict travel time. Oper Res Int J 24 , 55 (2024). https://doi.org/10.1007/s12351-024-00851-8

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Analytics as Code (AaC): How GoodData is Shaping the Future of Data Analytics

Written by Natalia Nanistova  |  Sep 16, 2024

The demand for streamlined, automated, and scalable analytics has never been greater. Why? As data volumes and complexity continue to skyrocket, traditional analytics processes are struggling to keep pace.

One transformative approach gaining traction in the analytics and business intelligence world is Analytics as Code (AaC) . This methodology treats analytics components such as data pipelines, transformations, and visualizations, as well as platform administration tasks such as environment building, management, and deployments as code. This revolutionizes how organizations manage and leverage their data; by embedding analytics within a code-centric framework, AaC empowers teams to accelerate development cycles, ensure consistency, and enhance collaboration across the organization.

What is Analytics as Code and why does it matter?

AaC applies software engineering principles to analytics processes. By managing analytics assets and entire environments as code, organizations enhance control, collaboration, and efficiency throughout the analytics lifecycle. Here’s why AaC stands out:

• Efficiency and automation: Automating repetitive tasks and workflows minimizes manual intervention, accelerates development, and reduces errors.
• Version control: Version control and code repository systems ensure that there’s a logged history of all data definitions and changes made, when, and by whom. This enables democratization and understanding of metrics while also providing a system of data governance.
• Scalability: AaC enables rapid scaling of analytics capabilities, by mixing and matching different composable elements for new purposes.
• Accuracy and quality: Automated testing and validation along with change logs help maintain high data quality and analytics reliability.

GoodData’s role in advancing Analytics as Code

GoodData is leading the way in adopting and implementing AaC by offering a robust platform designed to transform raw data into actionable insights with greater speed and precision. Here’s how GoodData supports the Analytics as Code approach:

• Comprehensive Toolset: GoodData facilitates a comprehensive analytics lifecycle through robust integrations and partner tools, covering data integration, modeling, visualization, and embedding. This approach leverages these integrations and partnerships to provide a flexible and controlled process, managed through code.
• Streamlined processes: GoodData’s platform supports the automation of key analytics tasks, including data refreshes and user creation. This automation reduces manual effort and enables rapid scaling, ensuring that analytics keep pace with business needs.
• API-first architecture: Built with an API-first approach, GoodData ensures seamless integration with other tools and systems. This architecture supports the modularity and interoperability essential to the AaC methodology.
• Collaboration: GoodData facilitates collaboration by providing a modular data app development platform. Data teams can work together more effectively, maintaining consistency across projects and reducing the risk of errors.
• User-friendly interface: While GoodData supports advanced AaC features, it also offers an intuitive interface, making it accessible to users with varying technical expertise. This balance ensures organizations can leverage AaC without requiring extensive coding skills across the board.

A real-world case study: enhancing efficiency with AaC at GoodData

The effectiveness of AaC can be demonstrated through a real-world situation. For example, consider our work with Mews, a B2B hospitality software company, that needed a scalable solution to manage its rapid growth and deliver financial and operational analytics to its customers. With over 5,000 properties and multi-property management complexities, Mews faced challenges in ensuring data consistency, efficient pipeline management, and customizable reporting across different time zones.

Mews implemented GoodData’s AaC functionality to automate data pipelines and embed insights directly into their existing hospitality management software. This integration enabled Mews to offer consistent, customizable analytics to their customers while improving operational efficiency. The GoodData platform also supports multitenancy , enabling Mews to provision and manage data for each customer centrally, while allowing for customizations.

Mews’ results:

• Reduced manual effort: The automation of data pipelines and the adoption of AaC reduced manual intervention and the associated risks of human error, ensuring data consistency across the board.
• Effective scalability: Mews can now seamlessly roll out new products and capabilities, offering customers a composable analytics approach that scales with their business needs.
• Improved team alignment: By embracing AaC, Mews enhanced communication and collaboration across teams, enabling effective experimentation and faster time-to-market for new features.

Could it be done manually? I don't think so. At this scale, we would very much struggle to support all the requests, onboarding, and development without an Analytics as Code approach.

Vojta Kopal, Director of Engineering, Data Science at Mews

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Understanding the AaC maturity model

To fully appreciate Analytics as Code, it’s useful to understand its maturity stages and how GoodData aligns with them:

• Adoption: Analytics are typically managed through basic GUI-based tools with limited development integration. GoodData enhances this stage by offering robust code-based integration, providing foundational support for scalability and flexibility.
• Exploration: At these levels, organizations adopt advanced methodologies such as API-first architectures, domain-specific languages (DSLs), and sophisticated version control. GoodData supports these needs with a comprehensive suite of tools that facilitate deeper integration and management of analytics processes.
• Full lifecycle integration: This represents the ideal state where analytics are seamlessly integrated throughout the entire development and deployment lifecycle. GoodData is paving the way toward this future by offering a fully integrated platform that supports continuous analytics delivery and adaptation.

Why now is the time to adopt AaC

The benefits of adopting Analytics as Code are substantial:

• Future-proofing your analytics: As data complexity grows, traditional methods become less effective. AaC offers a scalable, adaptable solution that evolves with your business.
• Maturity of AaC methodology: Infrastructure as Code (IaC) and Policy as Code (PaC) revolutionized IT and became the standard once the models had matured. Similarly, Analytics as Code is now hitting a point where it’s being accepted as a preferred approach by innovative analytics and product teams.
• Optimizing AI and ML integration: AaC offers a robust framework for managing the entire lifecycle and seamless integration of AI and ML models. It simplifies the integration of data pipelines and model deployments, accelerates iterative development, and supports real-time updates, ensuring your AI and ML initiatives are agile, accurate, and aligned with evolving business needs.

Try it with GoodData

Analytics as Code represents a key advancement in data analytics, delivering improvements in efficiency, accuracy, and scalability. GoodData’s platform aligns with AaC principles, offering tools that streamline analytics processes and support informed decision-making. With GoodData, you can enhance your analytics capabilities, accelerate insight generation, and stay competitive in a data-driven world. To try it for yourself, sign up for a 30-day trial .

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• Open access
• Published: 16 September 2024

Hybrid emergency care at the home for patients – A multiple case study

• Åsa Falchenberg   ORCID: orcid.org/0000-0001-8956-8011 1 , 2 ,
• Ulf Andersson   ORCID: orcid.org/0000-0002-1789-8158 1 , 3 ,
• Gabriella Norberg Boysen   ORCID: orcid.org/0000-0003-3203-3838 1 ,
• Henrik Andersson   ORCID: orcid.org/0000-0002-3308-7304 1 , 2 , 4 &
• Anders Sterner   ORCID: orcid.org/0000-0002-2430-5285 1 , 2  

BMC Emergency Medicine volume  24 , Article number:  169 ( 2024 ) Cite this article

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Introduction

Healthcare systems worldwide are facing numerous challenges, such as an aging population, reduced availability of hospital beds, staff reductions and closure of emergency departments (ED). These issues can exacerbate crowding and boarding problems in the ED, negatively impacting patient safety and the work environment. In Sweden a hybrid of prehospital and intrahospital emergency care has been established, referred to in this article as Medical Emergency Team (MET), to meet the increasing demand for emergency care. MET, consisting of physicians and nurses, moving emergency care from EDs to patients’ home. Physicians and nurses may encounter challenges in their healthcare work, such as limited resources for example medical equipment, sampling and examination, in unfamiliar varying home environments. There is a lack of knowledge about how these challenges can influence patient care. Therefore, the aim of this study was to explore the healthcare work of the METs when addressing patients’ emergency care needs in their homes, with a focus on the METs reasoning and actions.

Using a qualitative multiple case study design, two METs in southwestern Sweden were explored. Data were collected from September 2023 – January 2024 and consist of field notes from participant observations, short interviews and written reflections. A qualitative manifest content analysis with an inductive approach was used as the analysis method.

The result of this study indicates that physicians and nurses face several challenges in their daily work, such as recurring interruptions, miscommunication and faltering teamwork. Some of these problems may arise because physicians and nurses are not accustomed to working together as a team in a different care context. These challenges can lead to stress, which ultimately can expose patients to unnecessary risks.

When launching a new service like METs, which is a hybrid of prehospital and intrahospital emergency care, it is essential to plan and prepare thoroughly to effectively address the challenges and obstacles that may arise. One way to prepare is through team training. Team training can help reduce hierarchical structures by enabling physicians and nurses to feel that they can contribute, collaborate, and take responsibility, leading to a more dynamic and efficient work environment.

Peer Review reports

According to the World Health Organization (WHO), healthcare is facing several challenges, including an aging population [ 1 ] rising rates of chronic diseases, often characterized by exacerbation [ 2 ], which place greater demands on healthcare services. Simultaneously, the number of available hospital beds is decreasing, and due to staff cuts, there will be fewer ambulances and emergency departments (EDs) are closing [ 3 ]. In EDs, this leads to issues such as crowding and boarding, and which have a negative impact on the work environment such as workload that is too high, which may cause stress and risk of burnout [ 4 ]. Furthermore, crowding and boarding and have negative impacts on patient safety since of delays in medical treatment and inadequate monitoring, which can lead to increased mortality [ 5 ].

One way to meet patients’ needs for emergency care is to shift the care provided from the hospital to patients’ homes [ 6 , 7 ]. Offering home-based care (HBC) has been shown to be cost-effective [ 8 ] and safe for patients [ 9 ]. However, it may entail longer treatment times than hospital care, especially for certain chronic conditions [ 10 ]. Studies indicate that exacerbations of chronic conditions such as heart failure and chronic obstructive pulmonary disease, as well as pneumonias [ 11 ], symptoms such as fever, dyspnea [ 12 ], nonspecific symptoms in frail elderly patients, patients with cognitive impairment [ 13 ], and pain or injury to the skeletal or muscular system [ 14 ] can be effectively managed at patients homes. Currently, there is no consensus on what HBC entails or how it can be termed [ 15 ]. Terms such as “Hospital At Home” [ 9 , 16 ], “Same Day Emergency" [ 17 ], “Hospital In The Home” [ 18 ] or “Residential In Reach” [ 19 ] are used internationally, while in Sweden, general terms such as “Mobile teams”, “Mobile emergency teams”, or “Mobile home care teams” are used [ 20 ].

The Swedish healthcare system is divided into three levels of governance: state, region, and municipality. These levels are responsible for different parts of healthcare, specialized hospital care, primary care, and municipal care [ 21 ]. Currently, all levels are undergoing a transformation process called “good and integrative care” [ 22 ]. This initiative resembles the Integrated Care System in England [ 23 ] and aims to make healthcare more accessible and closer to the patient, focusing on their unique care needs [ 24 , 25 ]. As part of the Swedish transformation process to meet the increased need for emergency care, a hybrid of pre- and intrahospital emergency care has been established [ 25 ]. This hybrid version of emergency care will, in this article be referred to as the Medical Emergency Team (MET). The MET, consisting of two organizations, ambulance services (AS), and EDs, has merged and operates outside the hospital setting. MET is not the same as care provided by ambulance, primary or municipal care, MET is rather a combination of these services. The MET is staffed with ED physicians and nurses from the ED or AS and provide emergency care to patients who have suffered from sudden illness or injury [ 26 ] and operates wholly or partially from hospital-affiliated EDs.

When emergency care is provided in patients’ homes, a holistic approach is required to ensure that all aspects of patients’ care needs, including medical, caring, physical, psychological, social, and existential needs, are addressed [ 27 ]. This means that METs must be prepared to handle a wide range of care-related issues with limited resources, in an unfamiliar environment to ensure that the care provided in patients’ homes meets their needs [ 28 ]. This requires the MET to collaborate across boundaries both within the MET, and outside the team with other care providers such as AS, primary care or municipal care [ 25 , 29 ]. If the expectations of the MET’s care work, i.e., what they can do, are unclear, difficulties may arise [ 28 ]. In this study, healthcare work refers to performing various tasks which not only including technical skills such as collecting blood samples and managing medical equipment but also through understanding and responding to patients’ needs, both expressed and unexpressed. Furthermore, healthcare work includes communication within the MET, with patients and their relatives, as well as other healthcare actors. By examining how physicians and nurses reason and act when encountering patients’ care needs at home through the MET, obstacles and opportunities can be identified when hybrid emergency care is shifted to patients’ homes. The aim of this study was to explore the healthcare work of the METs when addressing patients’ emergency care needs in their homes, with a focus on the METs reasoning and actions.

Employing a qualitative multiple case study design [ 30 ], this study explored the MET as a contextually and socially bounded system [ 31 ]. The data were collected through participant observations, which enabled participation in daily activities, interactions, and events [ 32 ].

The research settings were two METs in the southwestern part of Sweden: MET A, which operated from a hospital-affiliated ED, and MET B, which operated from the AS. The possible assignments providers for MET A and MET Bs were similar. However, MET B could have paced assignments identified by the ED and AS when the MET was not operational. MET B could also be assigned to time-critical medical conditions to make initial assessments/treatments while waiting for AS. Primarily, the nurses were responsible for checking the equipment and restocking supplies in the vehicle. When the MET had no assignments, the physicians in the MET A supported their colleagues in the ED, carried out administrative tasks, and answered incoming calls to the MET. The physicians in MET B had administrative tasks and handled incoming calls to the MET when the team had no assignments. The two METs had varying conditions and staffing, and the equipment was slightly different between MET A and MET B, consisting of up to 13 different units. For more information see Table  1 .

Study participants and recruitment

The study received ethical approval from the Swedish Ethical Review Authority in Stockholm (NO: 2023-02186-01) and access to the research field was granted and formally approved by the managers of the participating facilities. All physicians and nurses who staffed the MET were invited to participate in the study. MET A was informed by the first author through a staff meeting and email, while MET B received the information verbally from the medical chief of the department. Each participant received both oral and written information about the study from the first author and signed a consent form. Other ethical considerations regarding data protection and data security were followed in accordance with the Swedish Data Protection Act [ 33 ]. All data are presented at the group level for the purpose of ensuring and maintaining the participants’ integrity and confidentiality, and the study aligns with accepted ethical principles for research [ 34 ]. The studies included five physicians and five nurses from MET A and five physicians and five nurses from MET B, see Table  2 for further information.

Data collection

The data were collected during the period from September 2023 to January 2024 and consisted of participant observations with field notes [ 32 ], interview notes [ 35 ] and written reflections [ 32 ].

Observations

The first author conducted all observations by following both METs for full work-shifts, and each patient visit was defined as one observation. The duration of the observations varied between the METs, se Table  3 . Physicians and nurses were encouraged to work as usual and to ignore the researcher, who aimed to maintain a low profile throughout. When arriving at the patient, the researcher was briefly introduced as a person who was there to observe how they worked.

All observations began when the MET received the assignment and ended when the door to the patients’ home closed. During the observation field notes were written containing what physicians and nurses said and how they reasoned when the assignment was received, during the assignment, and when it was completed. In total, 25 observation days were completed, comprising 73 observation instances. The observations lasted an average of 41 to 44 min and generated two to three pages of transcribed text, see Table  3 for further details.

To obtain a deeper understanding of METs reasoning about their actions when patients’ care needs were met, the following questions were asked; What are your thoughts about the assignment and what are your thoughts on the teamwork? Follow-up questions were posed in response to the answers given. To gain a deeper understanding, questions such as “Can you tell me more?” were used frequently. The interviews took place after the observations were completed, conducted in the car while leaving the patient.

After the completion of the observation and interviews, the first author wrote down reflections in a reflective text. The purpose of the reflection was to gain additional understanding of the research questions. These reflections were utilized in the discussion of the results.

Data Analysis

The collected data consisted of field notes, interview texts, and reflection texts were transcribed by the first author. During transcription, the text became more descriptive than the original because several fieldnotes were written with incomplete sentences when trying to write down as much as possible. The data were sorted into three phases of the MET assignment- preparatory, during the patient visit, and the reflection phase - which is a way to structure the data chronologically and provide organization [ 30 ]. To ensure that the analysis was as free as possible from interpretations, the author group discussed och reflected during the process. The qualitative manifest content analysis was conducted using an inductive apporach [ 36 ] and began with the first author reading the fieldnotes and interview texts multiple times to understand the content and obtain an overall sense of the data. In the second step, units from the text were extracted that addressed the aim of the study, to capture and describe METs healthcare work such as communication, physical actions, understanding and responding to patients’ care needs. These units were condensed without losing the content and coded based on their content. The codes were then sorted into categories and subcategories describing different aspects, similarities, or differences, ultimately forming four categories: Assignment reception and preparation phase, patient interaction and examination phase, decision-making and treatment phase and reflection and evaluation phase.

The results will be presented in chronological order, from when the METs receive the assignment until the assignment is completed, concluding with reflections from the METs. The results will include situational descriptions and quotes to present general patterns for MET A and MET B; unless otherwise specified, the aspects were the same. Each phase begins with a generic vignette that encompasses of several observation sessions. Individual observations are presented with the unique observations number.

Assignment reception and preparation phase

The METs are on their way to a patient , the physician reads loud from the patient’s medical record , the phone rings repeatedly , regarding new assignments and questions from AS , municipality care, etc. After each call , the physician gives a summary to the nurse. The nurse asks “inquisitively”… which patient are you referring to? The one we’re heading to , or is it another? Transportation time is then spent with the physician dictating notes in the patient’s medical records where recommendations to seek other levels of care or stay at home are given. When the nurse parks the vehicle outside the patient’s address , the METs discuss which equipment to bring.

Patient assignments could be provided at any time during the shift via phone or radio, and the information was sometimes vague or incomplete. The time for preparation varied depending on when the assignment was received, where the METs were geographically located in relation to the patient’s address, whether in an apartment in the same building or several kilometers away. Physicians received the most calls; occasionally, the speakerphone function was used so that the nurse could take part in the conversation and ask questions. On occasions when nurses answered the phone, a brief report was taken, and the nurse was asked to call back after consulting with the physician, or the phone was handed directly to the physician. Unlike MET A, MET B could receive assignments from the ED and AS when the MET was not operational. Messages were then written on notes handed over in person during shift changes at the ambulance station or at the ED. MET B could also be assigned to a critically ill patient, resulting in all delays for all other accepted assignments. On some days, assignments could pile up, causing patients to wait for several hours or for the METs to decline assignments. When assignments were received, the METs discussed the pros and cons to determine if it was a suitable patient; the physician had the authority to accept the assignment.

Nurses drove the vehicle, and transportation time could occur in silence, with the phone ringing incessantly, or with the METs discussing private matters. Physicians read and documented in the patient’s journal for upcoming and completed patient assignments. The METs could have difficulties finding the correct address; the functionality of the navigation system varied, and on several occasions, it did not work at all or provide incorrect directions. Upon arrival at the correct address, the need for additional information, such as a gate code or miscommunication regarding contagious patients, was discovered. When the vehicle was parked outside the patient’s residence, the decision on which equipment to use was made. Physicians were responsible for bringing the laptop bag and ultrasound equipment, while nurses were responsible for carrying other equipment. In instances where physicians were in an ongoing call, the nurse entered the patient’s home alone, but usually, the METs entered together.

Patient interaction and examination phase

When the METs entered the patient’s home , the physician approached first , either standing or squatting in front of the patient and said: Hello , my name is xxx and I am a physician , how are you? The nurse stands quietly behind , not wanting to interrupt the patient’s conversation with the physician and beginning to retrieve and set up the lab equipment. The physician examines the patient , is interrupted several times by phone calls , and then prescribes which tests to take. The nurse , who has been in another room , is not prepared for which tests to take and does not understand why.

When the METs arrived at the patient, they introduced themselves by name and title, and that they were from the MET. The physician was often the first to reach the patient. In instances where the MET had been assigned a critically ill patient, which was a part of MET B’s mission, there were usually already one or two ambulances on site. The physician then first contacted the ambulance nurse. When MET B was the first unit on site, the physician took the medication unit and went in alone to see the patient while the nurse parked the vehicle and brought in the rest of the equipment.

After the introduction, physicians usually immediately began gathering information about what had happened and how the patient was feeling. This meant that the nurse did not have a natural opportunity to greet, which could result in the nurse’s introduction occurring later during the visit or being completely omitted. Physicians often choose to sit down beside the patient or squat down. Before the examination, lights were sometimes turned on, blinds were pulled up, and the bed was raised. This was sometimes initiated by the patient, other individuals present, or the METs themselves. Examinations could also be conducted by leaning over the patient, in dim light where mobile flashlights were used to read vital signs. Depending on how many other people were in the room, information about the sequence of events could come from multiple sources. Nurses sometimes chose to listen as physicians gathered information, sometimes asked questions, or assisted when communication between the patient and physician did not work. When several people were present, it could sometimes become noisy in the room, resulting in the patient not hearing or understanding what the physician was asking, and the patient’s voice not being heard. The METs could be interrupted several times by phone calls with requests for new assignments, pending assignments, and advisory calls from AS.

The examinations were conducted based on the ABCDE principle (airway, breathing, circulation, disability, and exposure) and were carried out by a physician, while the nurse performed the examination, when agreed upon during the preparation phase. Physicians always listen to patients’ lungs. The nurse sometimes participated in the initial examination by handing equipment such as a stethoscope to the physician or standing quietly by the side and listening. Unlike in the MET A group physicians in the MET B group were interested in improving nurses’ examination techniques, such as listening to the lungs and interpreting electrocardiograms (ECG). Physicians encouraged the nurses to listen and report what they heard or allowed the nurses to make the initial assessment of the ECG. Different examination findings were discussed openly, which could lead to various expressions of curiosity and questions among those present. Most often, the nurse chose to begin measuring vital parameters (respiratory rate, saturation, blood pressure, pulse, and temperature) or to prepare the laboratory equipment during the ongoing examination. The cold blood pressure cuff was warmed on rare occasions. The clothing of patients could be partially or fully removed during the examination and was not routinely returned after the examination. Once vital parameters had already been taken, the nurse waited to take new until the physician indicated a desire for them. Nurses could express concerns about patients’ well-being, such as affected vital parameters during the ongoing examination, which the physician did not confirm or did not consider noteworthy.

The nurse measures the patient’s saturation… looks at the meter… furrows brow in concern , asks the patient to take a few deep breaths. Says to the physician: …are you noting the value? Yes , says the physician , who continues to sanitize the equipment [Observation 45].

Problems that could arise when vital parameters were taken included that they were often said out loudly in the room, which colleagues did not always hear. The values could be noted on journal sheets, pieces of paper, on gloves, or not at all. This resulted in uncertainties about which parameters had been taken and what they showed. The mission of MET B, unlike that of MET As, was to care for elderly patients. They could be interrupted during ongoing examinations to care for another patient, residing in the same assisted living facility, who had suddenly deteriorated. In those instances, the nurse stayed with the patient and continued the examination.

Sampling, which occurred after a physician’s order, was performed by nurses. Sometimes, the nurse could interrupt the ongoing examination to obtain blood samples without a physician’s order; other times, the nurse stood by and waited, ready with the sampling materials. When the nurse took the samples, the physician usually chose to sit down in another room to read the patient’s journal and plan for potential treatment. Nurses were responsible for retrieving the laboratory equipment and placing it where there was sufficient space, usually in an adjacent room; patients were then left alone while blood samples were analyzed. The results from sampling were crucial in some cases, such as when patients could not participate in the visit due to a disability. Blood samples could be taken via arterial, venous, or capillary methods, with the choice of method varying. In MET A, it was the patient’s symptoms and signs determine the choice of sampling method, while in MET B, arterial or capillary blood samples are usually taken. The reason for choosing the sampling technique was unfamiliarity with the venous sampling technique and the nurses’ interest in learning to collect arterial samples. This resulted in patients being punctured multiple times, and the decision regarding sampling could suddenly be re-evaluated when the sampling failed when there was a lack of available analysis material.

The issues that could arise with laboratory equipment included its sensitivity to cold temperatures and the shortage of the special cards. Attempts to warm the laboratory equipment were made by placing it near warm sources in the patient’s home, warming it against the body, and re-evaluating the need for sampling. MET A chose to place the sensitive equipment in another location in the vehicle, which MET B did not have the opportunity to do. The lab equipment was space-consuming, which challenged the METs in homes with many personal belongings and dirty surfaces. MET A, which had more lab equipment than MET B, forgot part of the equipment at the patients’ homes. METs can carry up to seven units into the patient, depending on the patient’s condition. Space constraints combined with large jackets during cold weather caused patients personal belongings to fall to the floor and break.

Decision-making and treatment phase

Physicians made decisions regarding treatment , which could involve medication , palliative care orders , expanded sampling , and continued hospital care. Physicians discussed treatment options with patients , when possible , as well as other healthcare personnel present. The nurse , who often remained in another room to manage patient sampling and pack equipment , did not hear the discussions and thus was unprepared for potential treatment and lacked knowledge of prescribed medications. When the decision for hospital transport was made , the nurse arranged it while the physician documented.

Decisions about treatment were typically made by physicians during the examination or sampling phase and could involve medication, expanded sampling, continued hospital care, palliative care orders, or observation. During this phase, METs could be interrupted repeatedly, resulting in incomplete perceptions of orders and important decisions made. Nurses repeated the current medication orders and awaited confirmation from the physician before administering the medication. Nurses could ask patients and relatives several questions but did not wait for or expect a response. Physicians usually provide medical self-care advice, while nurses ask if they have sufficient support from other healthcare providers. Nurses could also take initiative and suggest treatments to patients who had not communicated with the physician, which could sometimes lead to misunderstandings regarding patients’ degree of illness.

Patient with diarrhea , vomiting , high fever , and dizziness for five days. The patient said , “I find it hard to drink”. Nurses responded “ … it is a shame to go to the hospital , better to stay at home. You should take paracetamol and ibuprofen regularly throughout the day for the fever , and then you must drink properly , preferably soup or oral rehydration solution”. Meanwhile , the physician stands a short distance away , looks worried , makes a few attempts to intervene in the conversation but fails and eventually gives up [Observation 45].

Physicians typically proposed treatment options to patients, and in cases where patients had conditions such as impaired cognitive abilities or were in the end-of-life stage, they were not involved in decision-making. Decisions were discussed with other healthcare personnel if they were present. Relatives were involved when possible, and some decisions required physicians to try to help the patient understand, such as patients with mental health issues. The mission of MET B included, for example patients who experienced cardiac arrest and patients who died. During these missions, the MET took the time to talk about and support the relatives present and reassured them that the patient had not suffered.

The familiarity with handling the medications that METs carry varies. Nurses in the MET A were accustomed to administering the medications typically used in the ED, such as antibiotics, unlike those in the MET B. When questions and uncertainties arose regarding medications, which could concern how antibiotics should be diluted and administered, nurses consulted physicians, but they lacked practical knowledge. They then searched for information together on the internet or called the hospital’s ED for advice. On occasion, prescribed medication was not given because both the physician and the nurse lacked knowledge of how the medication should be administered.

Patient with suspected sepsis… the MET has called an ambulance… Physicians have ordered intravenous antibiotics. The nurse asks;… should we skip giving the antibiotics… the ambulance will be here soon? [Observation 42].

When multiple tasks needed to be performed, physicians could offer to administer medications. Since physicians were not familiar with the units containing medications and equipment, nurses had to interrupt their ongoing tasks to show the physician which unit the equipment was in and how it worked.

Patients who expressed insecurity about staying at home or being too ill were offered hospital care. The nurse arranged transportation to the hospital, assisted in moving the patient from, for example, the bed to the ambulance stretcher, and was responsible for filling out the journal sheet accompanying the patient to the hospital. The physician was responsible for documentation and contact with the receiving unit. When the physician had a probable working diagnosis and when there were available beds in the hospital wards, patients could be admitted directly. However, when there was a shortage of beds, which was common in MET A, or when the diagnosis was unclear, patients were transported to the hospital’s emergency department for further evaluation, treatment, and waiting for an available bed. The physicians were always documented in patients’ journals, while the extent of nurses’ routine documentation varied. The differences included nurses in the MET A documenting the reason for the visit, nursing status, entering test results, updating interventions from community care, and phone numbers for the patient and relatives in the patient’s hospital journal. MET B’s nurses documented by creating a case log in an ambulance journal, with reference to the physician’s notes in patients’ hospital journal.

The other healthcare providers with whom the METs collaborated with varied depending on the differences in the mission descriptions. Cooperation with municipal care was common, and physicians were responsible for handovers. MET visits often include takeovers, which could consist of newly prescribed medications, administration of antibiotics and intravenous fluids, as well as vital sign monitoring. There were regulations at certain special accommodations in MET B’s catchment area that governed, for example, the use of IV stands inside patients’ rooms. This resulted in the application rule being broken at the MET initiative when a patient needed intravenous fluids. The extent to which the prescribed medications were left varied. MET A left newly prescribed medications, either for the entire treatment period, which last up to 10 days, or for the first two to three days. Intravenous antibiotics were always left for the first day, then a follow-up visit was usually scheduled for the next day, or the patient could transition to oral treatment. In MET B, the first dose of antibiotics was given intravenously, and possibly the first tablet dose, with the remaining doses prescribed by the physician.

When the mission was considered completed, it was usually the nurse who sanitized the equipment and packed it. The MET usually said goodbye together and tried to restore the patient’s home to how it was when they arrived. Nevertheless, on occasion bright lights were forgotten to be turned off, the patient’s bed was not turned down, and that the patient would not become cold was not ensured. Usually, the nurses carried the equipment to the car, while the physician was responsible for the computer and printer and possibly the ultrasound on occasion.

Reflection and evaluation phase

The METs reflected on whether the mission had involved an ‘appropriate patient’ and considered whether additional examinations that the METs did not perform, such as X-rays, could have affected or improved the quality of care. Patient benefit was viewed as crucial, where the METs considered patients’ preferences alongside potential risks of staying at home, such as an increased risk of falling. The assigned missions often concerned patients who could be effectively treated at home, where a visit to the ED would not have added value.

The assignment involved a patient with addiction problems. The apartment was filled with cigarette smoke , with stacks of newspapers along the walls and personal belongings scattered everywhere. The MET had been contacted by home healthcare. The patient was not very responsive during the examination [Observation 9]. The doctor said; “The patient would have been sent to the ED if the MET had not assessed and treated the patient at home. However , an ED visit would not have made any difference to the patient’s outcome”. The nurse added: “I noticed he was so tired and lethargic… he seemed affected.” The physician responded , “…I had no thoughts of that at all” [Interview 9].

However, the METs also acknowledged that some missions required skills they did not possess, particularly in psychiatry. They expressed uncertainty about their role in certain missions and believed some were better suited for ambulance care, such as patients needing oxygen therapy. For patients requiring oxygen, the METs felt hospital care was necessary and that their involvement could delay treatment. Missions solely based on telephone assessments of patients’ needs were often considered less reliable compared to those assessed by licensed personnel on site. Patients’ emergency care needs varied, from requiring rapid hospital transport to care within primary care settings. The METs noted that some missions were not about providing home care but rather about optimizing ambulance resources, using methods like stretcher transport or a single-nurse ambulance. The METs agreed that in some cases, patients had waited too long for an ambulance and needed quicker intervention.

The METs expressed that within the team, there was an enabling and safe climate where they complemented each other and worked beyond professional boundaries, which they considered a strength. However, nurses sometimes felt that their skills were underutilized in missions that solely involved transporting physicians to patients. Nurses in the MET B group perceived ambiguity in their professional roles, while those in the MET A group experienced inequalities in task distribution. They expressed feeling responsible for multiple tasks, which could be time-consuming and challenging, such as checking vital signs, conducting tests, and addressing patients’ care needs, where they believed physicians could offer more support. The METs highlighted several strengths in teamwork, such as having one team member communicate with the patient to establish a strong connection and contribute different perspectives, with doctors focusing on the medical aspect and nurses on the care perspective. While the METs felt confident in the medical aspect, physicians found nursing tasks challenging, including assessing patients’ nutrition, elimination, personal hygiene, and fall risk assessment.

The mission involves an elderly patient in a nursing home with deteriorated general condition , diagnosed with dehydration by the time the MET leaves the patient [Observation 14]. On the way back , the nurse says; ”The patient resides in a facility , and it is not our responsibility to take over the facility’s duties. Since the patient did not express a desire for anything to drink , nursing interventions can be deprioritized in favor of other patients who are waiting [Interview 14].

The METs reflected on whether the decisions made were right or if they could have done things differently. Physicians in MET B viewed receiving many questions as positive because it prompted deeper thought. There was a clear need for confirmation among physicians during missions involving difficult-to-assess patients or making challenging decisions, such as end-of-life discussions and initiating palliative care orders. However, this need for confirmation was not always recognized by colleagues. Instead, nurses expressed concern about the lack of written information detailing the actions taken and the treatment plan implemented.

The results of the multiple case study indicate that physicians and nurses face several challenges in their daily work such as recurring interruptions, miscommunication and faltering teamwork. This can lead to stress, which not only exposes patients to unnecessary risks but also negatively affects physicians and nurses [ 37 ]. One way to attempt to understand and interpret the work systems within which physicians and nurses operate within is to investigate what happens within and outside the MET and how it can affect caregiving [ 29 ].

The results indicate that the MET could be interrupted multiple times during a patient visit by incoming calls regarding potential new patient assignments, ongoing consultations, or advisory calls from, for example, the AS. Additionally, as described in the assignment reception and preparation phase , MET B could be assigned to a critically ill patient. These interruptions could cause ongoing examinations to be disrupted and force physicians to start over, resulting in inefficient work. Constant interruptions can create feelings of losing control, leading to dissatisfaction and stress, which can result in burnout over time [ 38 ]. Emergency physicians and nurses are more frequently affected by burnout and emotional exhaustion [ 39 ]. Interruptions can negatively impact their ability to concentrate, potentially leading to inadequate or incorrect decisions regarding the care and treatment required for the patient’s condition [ 40 ]. In addition, the MET did not have necessary information such as access codes, and lacked knowledge about whether patients were carrying infectious diseases such as COVID-19 or gastroenteritis. Sometimes, the physician had received this information but had not shared it with the team. The failure to have such information exposed the MET to unnecessary risks of either contracting infections themselves or spreading them further. Previous research indicates, for example, that staff in AS are at greater risk of acquiring infections due to the uncontrolled environment in which they work [ 41 ].

Physicians were often the first to acknowledge the patient and would begin taking the medical history when MET arrived unless it involved a critically ill patient, which could be the case in MET B. On those occasions, as described in the patient interaction and examination phase , the physician took on a more withdrawn role. It was evident during the observations that the AS were accustomed to handling these situations and that the METs medical contribution was limited. Many patients who received care and treatment from the MET, especially MET B were elderly residents living in nursing homes. On several occasions, the MET expressed that these elderly patients were ideal candidates for emergency care at home, but also perceived that many of the visits would have been more appropriately managed by primary care. This is supported by previous research, which shows that emergency physicians and nurses perceived a lack of competence and insufficient involvement in patient care as contributing factors to AS being called out and the patient being transported to the ED [ 42 ].

During the examination, physicians might ask the nurse to measure vital signs, hand over a stethoscope, or remove the patient’s clothing to facilitate a more thorough examination This approach could be due by the fact that physicians working in EDs are accustomed to having limited time for gathering necessary information for making treatment and diagnosis decisions [ 43 ]. Medical history and examination results sometimes occurred simultaneously but could also occur separately. The questions asked were often open-ended, such as ”How are you feeling?” and ”Can you tell me why we are here today?”. Nurses often choose not to participate during the physician’s examination, as described in the patient interaction and examination phase . Instead, they prepared the lab equipment and carried out the physicians’ orders, acting as assistants. MET A, had more lab equipment to prepare than MET B, which could be time-consuming to unpack and set up. This withdrawn role that nurses sometimes adopted could lead to care becoming primarily medically focused, potentially overlooking patients’ comprehensive care needs. It is not surprising and not a new phenomenon that emergency care primarily has a medical focus [ 44 ]. Previous research shows that in EDs, there are deficiencies in both identifying and responding to patients’ fundamental care needs, such as nutrition, elimination, and fall prevention, which can lead to adverse events [ 45 ]. MET A was more likely to follow the ED’s routines and guidelines, such as documenting provided care and collecting blood cultures before administering intravenous antibiotics—a practice that was not followed at all in MET B. By adhering to these guidelines, MET A not only ensured compliance with established protocols but also enhanced patient safety. Guidelines are an essential tool for providing updated information and increasing the standard of [ 46 ]. In conclusion, while the medical focus in emergency care is undeniably important, integrating a comprehensive approach that includes adherence to guidelines is crucial, especially since this type of mobile care is primarily provided for frail elderly patients [ 25 ].

One way to increase patient safety and quality of care could be to work in teams [ 47 ] where collaboration is highly emphasized [ 25 ]. Collaborating is important in all care context, but is especially crucial in emergency care, where decision need to be made rapidly with limited information [ 48 ]. When emergency care is delivered in patients’ homes, MET face several challenges, including weighing the benefits and risks of providing care at home while also considering the patient’s wishes and autonomy [ 49 ]. The results of this multiple case study indicate that teamwork in the MET could be insufficient. Physicians and nurses had differing perceptions of the goal of the patient visit. A possible explanation for this could be a lack of sufficient communication between physicians and nurses. Nurses were not always involved when assignments were accepted, resulting in them having little or inadequate information when they arrived at the patients’ homes. During patient visits, physicians and nurses often worked separately, indicating a sequential working method, as described in the patient interaction and examination phase . A work system consists of several interdependent parts with various characteristics that rely on each other, making caregiving complex [ 29 ]. A sequential working method can thus contribute to unsynchronized, inefficient care, with risks for patient harm, such as missed nursing interventions or the failure to treat time-critical conditions according to standard protocols, such as early administration of antibiotics in suspected sepsis patients [ 50 ]. Another possible explanation for physicians and nurses working separately could be hierarchical structures within the MET. These hierarchical structures might have included ambiguities regarding professional roles and who was expected to be responsible for and carry out different parts of the healthcare work when identifying and meeting patients’ care needs [ 50 ]. In addition, previous research has highlighted the necessity of shared responsibility for patient care, which develops over time [ 51 ]. Another explanation could be that both METs were relatively new, involving a completely new way of working for which physicians’ and nurses were not trained for.

However, this study reveals that the phases described in the results can happen at any time and affect each other, underscoring the complexity the MET encounters when managing patients’ care at home. These factors, when combined, can negatively impact both care and patient safety [ 50 ] especially if the skills within the MET are not fully utilized. To address this, it is suggested that interprofessional simulation be implemented. This approach brings together different disciplines, allowing them to practice collaborative care in a controlled setting, which could enhance patient safety [ 52 ].

Strengths and limitations

A strength of this study is that it was conducted as a multiple case study, which is more compelling and robust than single case study [ 30 ]. Data also describe current phenomena in their real-world context, which is advantageous when the boundaries between the phenomenon and the context are unclear [ 30 , 32 ]. Another strength is that several approaches were used to gather data such as participant observations, short interviews and reflections. This enabled triangulation, which is a method used to explore complex phenomena that cannot be fully understood, with a single method or data source [ 53 ], can provide a broader and deeper understanding of physicians’ and nurses’ healthcare work in this hybrid form.

However, there are also some limitations to acknowledge. When the study was conducted, the METs were relatively new, which may have led to certain issues related to their ongoing development. To gain access to the research environments, gatekeepers were used. This can be seen as a weakness since gatekeepers are often key individuals within the organization with certain power, which may have influenced the participants to take part in the study to appease the chief. There were also differences in the number of observations between the METs. A reason for this was METs differed in missions and geographic catchment areas. The size of the area they served may have affected the number of completed observations due to the time they spent traveling between patients’ homes. Technical differences regarding the vehicles between the METs, as well as the inability to control incoming phone calls, may have resulted in important information being overlooked.

Finally, a limitation may be the professional role of the observer as a licensed nurse, which complicated maintaining the researcher role. On a few occasions, the first author had to abandon the observer role to assist with equipment and medication, which may have led to some data not being recorded. However, patient safety was a priority.

Conclusion & implications

This study highlighted the challenges physicians and nurses meet when a new service is launched in emergency care. The challenges include the expectation for physicians and nurses to collaborate in teams, ambiguity in job descriptions leads to inefficiencies and uncertainty. Moreover, physicians and nurses are not accustomed to working together, and team compositions change almost every shift. As a result, established work routines are difficult to maintain, requiring team members to constantly adapt to new colleagues and workflows.

It is also important to note that these challenges can contribute to increased stress levels among staff, which can negatively impact patient care. When there are deficiencies in communication and collaboration within the team, this can lead to mistakes or delays in care, exposing patients to unnecessary risks. To counteract these problems, it is crucial to invest in team training and to develop clear job descriptions and routines that support effective and coordinated teamwork. Team training can help reduce hierarchical structures by enabling physicians and nurses to feel that they can contribute, collaborate, and take responsibility, leading to a more dynamic and efficient work environment. By practicing reflection and feedback after completing assignments, a more inclusive and development-oriented environment can be fostered, which in turn can positively impact the care provided by METs.

In summary, the study shows that it is essential to place great emphasis on planning and preparation when introducing new forms of care such as MET. By ensuring that all team members are well-prepared and that there are clear structures and support in place, a more dynamic and efficient work environment that benefits both staff and patients can be created. This hybrid version of prehospital and intrahospital emergency care is a complement to traditional hospital care, ED, AS, primary and municipal care. This requires collaboration between different organizations and staff categories, where patients’ current needs and situations are the focus, without boundaries. Further research is needed to define or explain what MET entails or how it can be termed. Likewise, can physicians and nurses experience to meet patients emergency care needs at their homes provide valuable insights.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

World Health Organization. (WHO). Ageing and health. [Internet]. 2022 [cited 2024 May 1] https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

Sartini M, Carbone A, Demartini A, Giribone L, Oliva M, Spagnolo AM, et al. Overcrowding in Emergency Department: causes, consequences, and Solutions—A. Narrative Rev Healthc. 2022;10(9):1625.

Google Scholar  

Ström M. Stora neddragningar väntar 2024 trots kärva minusbudgetar. Läkartidningen. 2023.

Bütün A. Causes and solutions for emergency department crowding: a qualitative study of healthcare staff perspectives. Sürekli Tıp Eğitimi Dergisi. 2024;32(5):391–400. https://doi.org/10.17942/sted.1324994

Article   Google Scholar  

Eidstø A, Ylä-Mattila J, Tuominen J, Huhtala H, Palomäki A, Koivistoinen T. Emergency department crowding increases 10-day mortality for non-critical patients: a retrospective observational study. Intern Emerg Med. 2024;19(1):175–81. https://doi.org/10.1007/s11739-023-03392-8

Article   PubMed   Google Scholar  

Hollander JE, Sharma R. The availablists: emergency care without the emergency department. NEJM Catalyst Innovations Care Delivery 2021;2(6).

Hung WW, Ross JS, Farber J, Siu AL. Evaluation of the mobile acute care of the elderly (MACE) service. JAMA Intern Med. 2013;173(11):990–6. https://doi.org/10.1001/jamainternmed.2013.478

Article   PubMed   PubMed Central   Google Scholar  

Levine DM, Ouchi K, Blanchfield B, Saenz A, Burke K, Paz M, et al. Hospital-level care at home for acutely ill adults: a randomized controlled trial. Ann Intern Med. 2020;172(2):77–85. https://doi.org/10.7326/m19-0600

Leong MQ, Lim CW, Lai YF. Comparison of Hospital-at-home models: a systematic review of reviews. BMJ Open. 2021;11(1):e043285. https://doi.org/10.1136/bmjopen-2020-043285

Arsenault-Lapierre G, Henein M, Gaid D, Le Berre M, Gore G, Vedel I. Hospital-at-home interventions vs in-hospital stay for patients with chronic disease who present to the emergency department: a systematic review and meta-analysis. JAMA Netw open. 2021;4(6):e2111568–2111568. PMC8188269.

Leff B, Burton L, Mader SL, Naughton B, Burl J, Inouye SK, et al. Hospital at home: feasibility and outcomes of a program to provide hospital-level care at home for acutely ill older patients. Ann Intern Med. 2005;143(11):798–808. https://doi.org/10.7326/0003-4819-143-11-200512060-00008

Kuroda K, Miura T, Kuroiwa S, Kuroda M, Kobayashi N, Kita K. What are the factors that cause emergency home visit in home medical care in Japan? J Gen Family Med. 2021;22(2):81–6. PMC7921336.

Wolf LA, Lo AX, Serina P, Chary A, Sri-On J, Shankar K, et al. Frailty assessment tools in the emergency department: a geriatric emergency department guidelines 2.0 scoping review. J Am Coll Emerg Physicians Open. 2024;5(1):e13084. 10.1002/emp2.13084.

Joy T, Ramage L, Mitchinson S, Kirby O, Greenhalgh R, Goodsman D, Davies G. Community emergency medicine: taking the ED to the patient: a 12-month observational analysis of activity and impact of a physician response unit. Emerg Med J. 2020;37(9):530–9. https://doi.org/10.1136/emermed-2018-208394

Leff B, Montalto M. Home hospital-toward a tighter definition. J Am Geriatr Soc. 2004;52(12):2141. https://doi.org/10.1111/j.1532-5415.2004.52579_1.x

Levine DM, Pian J, Mahendrakumar K, Patel A, Saenz A, Schnipper JL. Hospital-Level Care at Home for acutely ill adults: a qualitative evaluation of a Randomized Controlled Trial. J Gen Intern Med. 2021;36(7):1965–73. https://doi.org/10.1007/s11606-020-06416-7

McNamara R, van Oppen JD, Conroy SP. Frailty same day emergency care (SDEC): a novel service model or an unhelpful distraction? Age Ageing. 2024;53(4). https://doi.org/10.1093/ageing/afae064

Department of Health. Hospital in the home, guidelines. [Internet] In: Victoria SoG, editor. 2011. [cited 2024 May 1] https://www.health.vic.gov.au/patient-care/hospital-in-the-home

The Royal Melbourne Hospital. RMH Residential In Reach. [Internet] 2024 [cited 2024 May 1] https://www.thermh.org.au/services/community-services/rmh-residential-in-reach

Torkelsson AK. Mobila team blir permanenta i Västmanland efter goda resultat. Läkartidningen 2024(5–6).

Sveriges kommuner och regioner. Så styrs sjukvården i Sverige. [Internet] 2022 [cited 2024 May 1] https://skr.se/skr/halsasjukvard/vardochbehandling/ansvarsfordelningsjukvard.64151.html

Regeringskansliet. God och nära vård - en primärvårdsreform. [Internet] In: Socialdepartement, editor. Stockholm; 2018. [cited 2024 May 1] https://www.regeringen.se/rattsliga-dokument/statens-offentliga-utredningar/2018/06/sou-201839/

van der Feltz-Cornelis C, Attree E, Heightman M, Gabbay M, Allsopp G. Integrated care pathways: a new approach for integrated care systems. Br J Gen Pract. 2023;73(734):422. https://doi.org/10.3399/bjgp23X734925

Eriksson K. The theory of Caritative Caring: a vision. Nurs Sci Q. 2007;20(3):201–2. https://doi.org/10.1177/0894318407303434

Teske C, Mourad G, Milovanovic M. Mobile care - a possible future for emergency care in Sweden. BMC Emerg Med. 2023;23(1):80. https://doi.org/10.1186/s12873-023-00847-1

Riksföreningen för akutsjuksköterskor & Svensk sjuksköterskeförening. Kompetensbeskrivning. Legitimerad sjuksköterska med specialistsjuksköterskeexamen med inriktning mot akutsjukvård. [Internet] In: sjuksköterskeförening RfaS, editor. 2017.[cited 2024 May 1] https://swenurse.se/publikationer/kompetensbeskrivning-for-sjukskoterskor-inom-akutsjukvard

Kitson A. The fundamentals of care framework as a point-of-care nusring theory. Nurs Res. 2018;67:99–107. https://doi.org/10.1097/nnr.0000000000000271

Barker RO, Stocker R, Russell S, Hanratty B. Future-proofing the primary care workforce: a qualitative study of home visits by emergency care practitioners in the UK. Eur J Gen Pract. 2021;27(1):68–76. https://doi.org/10.1080/13814788.2021.1909565

Holden R, Carayon P. SEIPS 101 and seven simple SEIPS tools. BMJ Qual Saftey. 2021;30901–10. https://doi.org/10.1136/bmjqs-2020-012538

Yin RK. Case Study Research and Applications: design and methods. Sixth edition. ed. Thousand Oaks: SAGE Publications, Incorporated; 2017.

Carayon P. The Balance Theory and the Work System Model ... Twenty Years Later. International journal of human-computer interaction 2009;25(5):313–327. Doi:10.1080/10447310902864928.

Fangen K. Deltagande observation. Liber AB; 2005.

Lag med kompletterande bestämmelser till EU:s dataskyddsförordning (SFS. 2018:218) [Internet]. Stockholm: Sveriges Riksdag [cited 2024 May 1]. https://www.riksdagen.se/sv/dokument-och-lagar/dokument/svensk-forfattningssamling/lag-2018218-med-kompletterande-bestammelser_sfs-2018-218/

World Medical Association (WMA). Declaration of Helsinki – Ethical Principles for Ethical Research Involving Human Subjects. [Internet] 2022 [cited 2024 May 1]. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/

Polit DF, Beck CT. Nursing research: generating and assessing evidence for nursing practice. Wolters Kluwer; 2021.

Erlingsson C, Brysiewicz P. A hands-on guide to doing content analysis. Afr J Emerg Med. 2017;7(3):93–9. https://doi.org/10.1016/j.afjem.2017.08.001

Zabin LM, Zaitoun RSA, Sweity EM, de Tantillo L. The relationship between job stress and patient safety culture among nurses: a systematic review. BMC Nurs. 2023;22(1):39. https://doi.org/10.1186/s12912-023-01198-9

Rick VB, Brandl C, Knispel J, Slavchova V, Arling V, Mertens A, Nitsch V. What really bothers us about work interruptions? Investigating the characteristics of work interruptions and their effects on office workers. Work Stress 2024:1–25. https://doi.org/10.1080/02678373.2024.2303527

Somville F, Van Bogaert P, Wellens B, De Cauwer H, Franck E. Work stress and burnout among emergency physicians: a systematic review of last 10 years of research. Acta Clin Belg. 2024;79(1):52–61.

Article   CAS   PubMed   Google Scholar  

Shan Y, Shang J, Yan Y, Ye X. Workflow interruption and nurses’ mental workload in electronic health record tasks: an observational study. BMC Nurs. 2023;22(1):63. https://doi.org/10.1186/s12912-023-01209-9

Thomas B, O’Meara P, Spelten E. Everyday dangers – the Impact Infectious Disease has on the health of paramedics: a scoping review. Prehosp Disaster Med. 2017;32(2):217–23. https://doi.org/10.1017/s1049023x16001497

Lemoyne S, Van Bastelaere J, Nackaerts S, Verdonck P, Monsieurs K, Schnaubelt S. Emergency physicians’ and nurses’ perception on the adequacy of emergency calls for nursing home residents: a non-interventional prospective study. Front Med. 2024;11. https://doi.org/10.3389/fmed.2024.1396858

Roh H, Park KH. A scoping review: communication between Emergency Physicians and patients in the Emergency Department. J Emerg Med. 2016;50(5):734–43. https://doi.org/10.1016/j.jemermed.2015.11.002

Falchenberg Å, Andersson U, Wireklint Sundström B, Bremer A, Andersson H. Clinical practice guidelines for comprehensive patient assessment in emergency care: a quality evaluation study. Nordic J Nurs Res. 2021;41(4):207–15. https://doi.org/10.21203/rs.3.rs-74914/v1

Duhalde H, Bjuresäter K, Karlsson I, Bååth C. Missed nursing care in emergency departments: a scoping review. Int Emerg Nurs. 2023;69:101296. https://doi.org/10.1016/j.ienj.2023.101296

Jones ES, Rayner BL. The importance of guidelines. Cardiovasc J Afr. 2014;25(6):296–7. PMC10090964.

PubMed   PubMed Central   Google Scholar  

Salas E, Burke CS, Cannon-Bowers JA. Teamwork: emerging principles. Int J Manage Reviews. 2000;2(4):339–56. https://doi.org/10.1111/1468-2370.00046

Hagiwara MA, Nilsson L, Strömsöe A, et al. Patient safety and patient assessment in pre-hospital care: a study protocol. Scandinavian J Trauma Resusc Emerg Med. 2016;24(14). https://doi.org/10.1186/s13049-016-0206-7

Forsgärde E-S, Rööst M, Elmqvist C, Fridlund B, Svensson A. Physicians’ experiences and actions in making complex level-of-care decisions during acute situations within older patients’ homes: a critical incident study. BMC Geriatr. 2023;23(1):323.

Essex R, Kennedy J, Miller D, Jameson J. A scoping review exploring the impact and negotiation of hierarchy in healthcare organisations. Nurs Inq. 2023;30(4):e12571.

Burrell A, Scrimgeour G, Booker M. GP roles in emergency medical services: a systematic mapping review and narrative synthesis. BJGP Open 2023;7(2).

Bell R, Fredland N. The Use of theoretical frameworks Guiding Interprofessional Simulation: an integrative review. Nurs Educ Perspect. 2020;41(3):141–5.

Heale R, Forbes D. Understanding triangulation in research. Evid Based Nurs. 2013;16(4):98–98.

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Acknowledgements

Acknowledgements The authors would like to express their deepest gratitude to the physicians and nurses who participated in this study. It was a privilege to take part in your daily work as well to listening to your thoughts on the research topic.

No funding was received for conducting this study. Open access funding was provided by the University of Borås.

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Authors’ contributions The study design was proposed by ÅF, GNB, HA and AS. The observation and interview guide were designed by ÅF, GNB, HA and AS and the observations and interviews were performed by ÅF. The data analysis and interpretation of data was performed by ÅF and further was discussed with UA and AS. ÅF drafted the manuscript, and AS and UA substantively revised it. All authors read and approved the submitted version of the manuscript.

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Falchenberg, Å., Andersson, U., Boysen, G.N. et al. Hybrid emergency care at the home for patients – A multiple case study. BMC Emerg Med 24 , 169 (2024). https://doi.org/10.1186/s12873-024-01087-7

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Walkability in riyadh: a comprehensive assessment and implications for sustainable community—al-falah case study.

1. Introduction

2. the current urban landscape in riyadh, 2.1. challenges of creating walkable communities in riyadh, 2.2. future development plans and initiatives, 3. methodology of investigation, 3.1. theoretical investigation—walkability as a sustainable urban development, 3.1.1. walkability defined, 3.1.2. benefits of walkability toward sustainable urban development.

• B. Environmental Benefits
• C. Economic Benefits
• D. Social Benefits

4. Thematic Outcome of the Theoretical Investigation—Criteria Definition

4.1. quality of the journey, 4.2. urban form, 4.3. pedestrian infrastructure, 5. al-falah neighborhood: demonstrating the assessment of sustainable community, 5.1. implementing the criteria and indicators of the quality of the journey, 5.2. exploring the indicators of the “quality of the journey” walkability theme—visual and field analysis—the applicability of the walkability theme in quarters 1 & 2 of al-falah neighborhood, 6. results and interpretative discussion, 6.1. criterion 1 interpretation: safety and security, 6.2. criterion 2 interpretation: comfort, 6.3. criterion 3 interpretation: aesthetic appreciation, 7. conclusive recommendations and guidelines, 7.1. recommendations for future action for promoting walkable communities in riyadh.

• Reducing the risk of tripping on the raised crosswalks by minimizing the gaps between the sidewalks and the crosswalks;
• The provision of sufficient parking spaces will reduce the instances of parking cars on the sidewalks. Another measure that could be implemented in this regard is limiting private car ownership to one or two per household;
• Considering densification as a main urban strategy in which compact mixed-use neighborhoods and developments are realized. The developments that combine residential, commercial, and recreational spaces within walkable distances can contribute to a vibrant and active community. Consequently, this will mitigate the need to travel by car and enhance a sense of community, belonging, and place attachment;
• Integrating public transportation systems, such as an efficient network of buses or a light-rail system, can also facilitate access to different parts of the city and reduce car dependency;
• Increasing connectivity with adjacent neighborhoods by providing safe crossings for pedestrians (i.e., bridges or underground tunnels);
• Considering neighborhood design that is inspired by traditional local neighborhoods in which safety measures are ingeniously achieved through hierarchical design strategies that filtrate pedestrian movement from the public to the private. Also, seeking inspiration from traditional local architecture, reinterpreting design strategies that enhance privacy but at the same time allow for the surveillance of adjacent paths;
• The provision of street furniture that enhances the pedestrian experience (i.e., setting areas and benches along the main and secondary streets, more electric bikes and scooters in different locations in the neighborhood);
• The provision of wind and shading devices along the main and secondary streets;
• Enhancing the plantation of trees for shading along the main and secondary streets;
• The provision of public restrooms in multiple locations along the main and secondary streets;
• Activating public facades will significantly enhance engaging the inhabitants with the public spaces and other inhabitants. This could be achieved through the provision of attractive translucent shopfronts and extending the public spaces to the sidewalks;
• Creating a distinct neighborhood identity inspired by the critical interpretation of traditional local neighborhoods and architecture. This critical design approach incorporates and ensures the continuity of the local identity of Najd, which is deeply rooted in history and rich in typological and spatial solutions. It critically analyzes and understands Najdi architecture and community planning as rich sources of inspiration for contemporary solutions and design propositions. The goal is not to emulate but to ingeniously translate these sources in a critical, innovative, and contemporary manner. This critical continuity with the past is crucial for place attachment and sense of belonging, community building, and social cohesion.

7.2. Potential Strategies Guides for Promoting Walkable Communities in Riyadh

Author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• McCormack, G.R.; Blackstaffe, A.; Nettel-Aguirre, A.; Csizmadi, I.; Sandalack, B.; Uribe, F.A.; Rayes, A.; Friedenreich, C.; Potestio, M.L. The Independent Associations between Walk Score and Neighborhood Socioeconomic Status, Waist Circumference, Waist-To-Hip Ratio and Body Mass Index Among Urban Adults. Int. J. Environ. Res. Public Health 2018 , 15 , 1226. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Giles-Corti, B.; Broomhall, M.H.; Knuiman, M.; Collins, C.; Douglas, K.; Ng, K.; Lange, A.; Donovan, R.J. Increasing walking: How important is distance to, attractiveness, and size of public open space? Am. J. Prev. Med. 2005 , 28 (Suppl. 2), 169–176. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Frank, L.D.; Sallis, J.F.; Conway, T.L.; Chapman, J.E.; Saelens, B.E.; Bachman, W. Many Pathways from Land Use to Health: Associations between Neighborhood Walkability and Active Transportation, Body Mass Index, and Air Quality. J. Am. Plan. Assoc. 2006 , 72 , 75–87. [ Google Scholar ] [ CrossRef ]
• Litman, T. Transportation Cost and Benefit Analysis II—Travel Impacts and Value of Time Savings ; Victoria Transport Policy Institute: Victoria, BC, Canada, 2023; Available online: www.vtpi.org (accessed on 12 January 2024).
• Jacobs, J. The Death and Life of Great American Cities ; Vintage Books: New York, NY, USA, 1961. [ Google Scholar ]
• Ewing, R.; Schmid, T.; Killingsworth, R.; Zlot, A.; Raudenbush, S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am. J. Health Promot. 2003 , 18 , 47–57. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cao, X.; Mokhtarian, P.L.; Handy, S.L. Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach. Transportation 2007 , 34 , 535–556. [ Google Scholar ] [ CrossRef ]
• Carr, L.J.; Dunsiger, S.I.; Marcus, B.H. Validation of Walk Score for estimating access to walkable amenities. Br. J. Sports Med. 2011 , 45 , 1144–1148. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ PubMed Central ]
• Besser, L.M.; Dannenberg, A.L. Walking to public transit: Steps to help meet physical activity recommendations. Am. J. Prev. Med. 2005 , 29 , 273–280. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Saelens, B.E.; Sallis, J.F.; Black, J.B.; Chen, D. Neighborhood-based differences in physical activity: An environment scale evaluation. Am. J. Public Health 2003 , 93 , 1552–1558. [ Google Scholar ] [ CrossRef ]
• Mostafa, L. Investigating the Impact of Saudi QoL Program on Neighborhoods Public Spaces: Riyadh neighborhoods. Environ.-Behav. Proc. J. 2021 , 6 , 293–303. [ Google Scholar ] [ CrossRef ]
• Al-Hathloul, S.; Riyadh Architecture in One Hundred Years. A Public Lecture Curated by Center for the Study of the Built Environment at Darat Al-Funun, Amman, Jordan in 2002. Available online: https://www.csbe.org/riyadh-architecture-in-one-hundred-years-1 (accessed on 20 March 2022).
• Ministry of Municipal and Rural Affairs (MOMRA). Riyadh City Profile 2019. UN Habitat. Available online: https://unhabitat.org/riyadh-city-profile (accessed on 19 June 2023).
• Riyadh Municipality. (n.d.). Riyadh Traffic Safety. 2024. Available online: https://www.alriyadh.gov.sa/en/Divisions/Traffic/Riyadh%20Traffic%20Safety/Pages/default.aspx (accessed on 30 March 2024).
• Aina, Y.A.; Merwe, J.H.V.; Alshuwaikhat, H.M. Urban spatial growth and land use change in Riyadh: Comparing spectral angle mapping and band ratioing techniques. In Proceedings of the Academic Track of the 2008 Free and Open-Source Software for Geospatial (FOSS4G) Conference, Incorporating the GISSA Conference, Cape Town, South Africa, 29 September–4 October 2008; pp. 51–57. [ Google Scholar ]
• Almatar, K.M. Rehumanize the Streets and Make Them More Smart and Livable in Arab Cities: Case Study: Tahlia Street; Riyadh City, Saudi Arabia. Sustainability 2024 , 16 , 3376. [ Google Scholar ] [ CrossRef ]
• Aina, Y.A.; Adam, E.M.I.; Ahmed, F.; Wafer, A.; Alshuwaikhat, H.B. Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia. Eur. J. Remote Sens. 2019 , 52 , 557–571. [ Google Scholar ] [ CrossRef ]
• Ledraa, T.A. An Examination of Riyadh Neighborhood Forms Using GIS Applications. Emir. J. Eng. Res. (EJER) 2015 , 20 , 15–31. [ Google Scholar ] [ CrossRef ]
• Al Zohbi, G. Sustainable Transport Strategies: A Case Study of Riyad, Saudi Arabia. E3S Web Conf. 2021 , 259 , 02007. [ Google Scholar ] [ CrossRef ]
• Riyadh Development Authority. Riyadh Metro. 2020. Available online: http://www.ada.gov.sa/ (accessed on 12 January 2024).
• Youssef, Z.; Alshuwaikhat, H.; Reza, I. Modeling the Modal Shift towards a More Sustainable Transport by Stated Preference in Riyadh, Saudi Arabia. Sustainability 2021 , 13 , 337. [ Google Scholar ] [ CrossRef ]
• Almughairy, A. Reinventing Urban Space in Riyadh’s Neighborhoods. J. Archit. Plan. Res. 2017 , 34 , 295–308. [ Google Scholar ]
• Alamri, S. Spatial Analysis and GIS Mapping of Public Parks Adequacy: A Case Study from Riyadh, Saudi Arabia. Sustainability 2024 , 16 , 3305. [ Google Scholar ] [ CrossRef ]
• Almayouf, A. Preserving the Green in Hot-arid Desert Environments: The Case of Riyadh, Saudi Arabia. J. King Saud. Univ. Archit. Plan. 2013 , 25 , 39–49. [ Google Scholar ]
• Alznafer, B.M. The Impact of Neighbourhood Geometries on Outdoor Thermal Comfort and Energy Consumption from Urban Dwellings: A Case Study of the Riyadh City, the Kingdom of Saudi Arabia. PhD Thesis, Cardiff University, Cardiff, UK, 2014. [ Google Scholar ]
• Al-Mosaind, M. Applying complete streets concept in Riyadh, Saudi Arabia: Opportunities and challenges. Urban. Plan. Transp. Res. 2018 , 6 , 129–147. [ Google Scholar ] [ CrossRef ]
• Al-Rashdi, N.A.S.; Abdelwahed, N.A.A. The Empowerment of Saudi Arabian Women through a Multidimensional Approach: The Mediating Roles of Self-Efficacy and Family Support. Sustainability 2022 , 14 , 16349. [ Google Scholar ] [ CrossRef ]
• Hidayat, N.; Haryanto, I.; Isheka, R.P. Evaluation of Pedestrian Facilities Performance. IOP Conf. Ser. Earth Environ. Sci. 2022 , 1065 , 012061. [ Google Scholar ] [ CrossRef ]
• Sultan, B.; Katar, I.N.; Al-Atrush, M.E. Towards sustainable pedestrian mobility in riyadh city, Saudi Arabia: A case study. Sustain. Cities Soc. 2021 , 69 , 102831. [ Google Scholar ] [ CrossRef ]
• Katar, I. Promoting pedestrian ecomobility in Riyadh City for sustainable urban development. Sci. Rep. 2022 , 12 , 14808. [ Google Scholar ] [ CrossRef ]
• Sreedhara, M.; Valentine Goins, K.; Frisard, C.; Rosal, M.C.; Lemon, S.C. Stepping Up Active Transportation in Community Health Improvement Plans: Findings from a National Probability Survey of Local Health Departments. J. Phys. Act. Health 2019 , 16 , 772–779. [ Google Scholar ] [ CrossRef ]
• Parashar, A.; Bnayan, H. Studying walkability preferences using urban design qualities: A Case of Riyadh, Saudi Arabia. IOP Conf. Ser. Earth Environ. Sci. 2020 , 452 , 012140. [ Google Scholar ] [ CrossRef ]
• The Royal Commission for Riyadh City. Riyadh Comprehensive Strategic Plan. 2030. 2017. Available online: https://www.rcrc.gov.sa/reports/Riyadh-Comprehensive-Development-Plan-2030_EN.pdf (accessed on 12 January 2024).
• Al-Zoabi, A.; Jarrar, O.M. Sustainable City Paradigm: Criteria and Indicators of Efficiency. In WIT Transactions on Ecology & Environment ; Garrigos, A., Brebbia, C., Eds.; WIT Press: Southampton, UK, 2016; Volume 204, pp. 143–159. [ Google Scholar ]
• Frank, L.D.; Andresen, M.A.; Schmid, T.L. Obesity relationships with community design, physical activity, and time spent in cars. Am. J. Prev. Med. 2004 , 27 , 87–96. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Saelens, B.E.; Handy, S.L. Built environment correlates of walking: A review. Med. Sci. Sports Exerc. 2008 , 40 , S550–S566. [ Google Scholar ] [ CrossRef ]
• Handy, S.L.; Boarnet, M.G.; Ewing, R.; Killingsworth, R.E. How the built environment affects physical activity: Views from urban planning. Am. J. Prev. Med. 2002 , 23 (Suppl. 2), 64–73. [ Google Scholar ] [ CrossRef ]
• Ledraa, T.A. Evaluating Walkability at The Neighborhood and Street Levels in Riyadh Using GIS and Environment Audit Tools. Emir. J. Eng. Res. 2015 , 20 , 1–13. [ Google Scholar ]
• Ewing, R.; Cervero, R. Travel and the Built Environment: A Meta-Analysis. J. Am. Plan. Assoc. 2010 , 76 , 265–294. [ Google Scholar ] [ CrossRef ]
• Project for Public Spaces (PPS). What Is Walkability? How Do You Measure It? Available online: https://www.pps.org/article/what-is-walkability-how-do-you-measure-it-take-aways-from-this-years-trb-meeting (accessed on 12 January 2024).
• Frank, L.D.; Schmid, T.L.; Sallis, J.F.; Chapman, J.; Saelens, B.E. Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. Am. J. Prev. Med. 2005 , 28 , 117–125. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Giles-Corti, B.; Vernez-Moudon, A.; Reis, R.; Turrell, G.; Dannenberg, A.L.; Badland, H.; Foster, S.; Lowe, M.; Sallis, J.F.; Stevenson, M.; et al. City planning and population health: A global challenge. Lancet 2016 , 388 , 2912–2924. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Adkins, A.; Makarewicz, C.; Scanze, M.; Ingram, M.; Luhr, G. Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context? J. Am. Plan. Assoc. 2017 , 83 , 296–314. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kelly, C.M.; Lian, M.; Struthers, J.; Kammrath, A. Walking to Work: The Roles of Neighborhood Walkability and Socioeconomic Deprivation. J. Phys. Act. Health 2015 , 12 (Suppl. 1), S70–S75. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guite, H.F.; Clark, C.; Ackrill, G. The impact of the physical and urban environment on mental well-being. Public Health 2006 , 120 , 1117–1126. [ Google Scholar ] [ CrossRef ]
• Mair, C.; Diez Roux, A.V.; Galea, S. Are Neighbourhood Characteristics Associated with Depressive Symptoms? A Review of Evidence. J. Epidemiol. Community Health 2008 , 62 , 940–946; 8p following 946. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van den Bosch, M.; Ode Sang, A. Urban natural environments as nature-based solutions for improved public health—A systematic review of reviews. Environ. Res. 2017 , 158 , 373–384. [ Google Scholar ] [ CrossRef ]
• Board, T.R. National Academies of Sciences, Engineering and Medicine. In Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects ; The National Academies Press: Washington, DC, USA, 2004. [ Google Scholar ] [ CrossRef ]
• Du, X.; Kang, X.; Gao, Y.; Wang, X. Driving behavior characterization and traffic emission analysis considering the vehicle trajectory. Front. Psychol. 2024 , 14 , 1341611. [ Google Scholar ] [ CrossRef ]
• Juul, V.; Nordbø, E.C.A. Examining activity-friendly neighborhoods in the Norwegian context: Green space and walkability in relation to physical activity and the moderating role of perceived safety. BMC Public Health 2023 , 23 , 259. [ Google Scholar ] [ CrossRef ]
• Cohen, D.A.; Han, B.; Derose, K.P.; Williamson, S.; Marsh, T.; McKenzie, T.L. Physical activity in parks: A randomized controlled trial using community engagement. Am. J. Prev. Med. 2013 , 45 , 590–597. [ Google Scholar ] [ CrossRef ]
• Ewing, R.; Bartholomew, K.; Winkelman, S.; Walters, J.; Chen, D. Growing Cooler: The Evidence on Urban Development and Climate Change ; Urban Land Institute: Washington, DC, USA, 2008; ISBN 9780874201789. [ Google Scholar ]
• Shephard, R.J. The Exercising Commuter: Is Commuting a Healthy Way to Be Active? Curr. Cardiovasc. Risk Rep. 2012 , 6 , 299–306. [ Google Scholar ] [ CrossRef ]
• Henriques-Neto, D.; Peralta, M.; Garradas, S.; Pelegrini, A.; Pinto, A.A.; Sánchez-Miguel, P.A.; Marques, A. Active Commuting and Physical Fitness: A Systematic Review. Int. J. Environ. Res. Public Health 2020 , 17 , 2721. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yameqani, A.S.; Alesheikh, A.A. Development and evaluation of a walkability index (Case Study: Districts of the Ghom city). JGST 2015 , 5 , 159–174. Available online: http://jgst.issgeac.ir/article-1-236-en.html. (accessed on 12 January 2024).
• Leinberger, C.; Alfonzo, M. Walk This Way: The Economic Promise of Walkable Places in Metropolitan Washington, D.C. ; The Brookings Institution: Washington, DC, USA, 2012; Available online: https://www.brookings.edu/wp-content/uploads/2016/06/25-walkable-places-leinberger.pdf. (accessed on 12 January 2024).
• Cortright, J. Walking the Walk: How Walkability Raises Home Values in U.S. Cities ; CEOs for Cities; Impresa Inc.: Chicago, IL, USA, 2009; Available online: http://www.ceosforcities.org/pagefiles/WalkingTheWalk_CEOsforCities.pdf (accessed on 12 January 2024).
• Litman, T. Evaluating Public Transportation Health Benefits ; Victoria Transport Policy Institute: Victoria, BC, Canada, 2020; Available online: https://www.vtpi.org/tran_health.pdf (accessed on 12 January 2024).
• Burchell, R.W.; Downs, A.; McCann, B.; Mukherji, S. Sprawl Costs: Economic Impacts of Unchecked Development ; Island Press: Washington, DC, USA, 2005. [ Google Scholar ]
• McCann, B. Driven to Spend: The Impact of Sprawl on Household Transportation Expenses ; Surface Transportation Policy Project, Center for Neighborhood Technology: Chicago, IL, USA, 2000; Available online: https://www.walkboston.org/sites/default/files/DriventoSpend.pdf (accessed on 14 January 2024).
• Litman, T.A. Economic value of walkability. Transp. Res. Rec. J. Transp. Res. Board 2003 , 1828 , 3–11. [ Google Scholar ] [ CrossRef ]
• Southworth, M.F. Designing the walkable city. J. Urban Plan. Dev. 2005 , 131 , 246–257. [ Google Scholar ] [ CrossRef ]
• Leyden, K.M. Social capital and the built environment: The importance of walkable neighborhoods. Am. J. Public Health 2003 , 93 , 1546–1551. [ Google Scholar ] [ CrossRef ]
• Saelens, B.E.; Sallis, J.F.; Frank, L.D. Environmental correlates of walking and cycling findings from the transportation, urban design, and planning literatures. Ann. Behav. Med. A Publ. Soc. Behav. Med. 2003 , 25 , 80–91. [ Google Scholar ] [ CrossRef ]
• Zhu, X.; Lu, Z.; Yu, C.Y.; Lee, C.; Mann, G. Walkable communities: Impacts on residents’ physical and social health: Researchers from Texas A&M University studied residents in a newly developed ‘walkable community’ in Austin, Texas to see how it changed their habits for physical activity and whether it increased social interaction and cohesion in the community. World Health Des. 2013 , 6 , 68–75. [ Google Scholar ] [ PubMed ]
• Jun, H.-J.; Hurr, M. The relationship between walkability and neighborhood social environment: The importance of physical and perceived walkability. Appl. Geogr. 2015 , 62 , 115–124. [ Google Scholar ] [ CrossRef ]
• Wasfi, R.A.; Ross, N.A.; El-Geneidy, A.M. Achieving recommended daily physical activity levels through commuting by public transportation: Unpacking individual and contextual influences. Health Place 2013 , 23 , 18–25. [ Google Scholar ] [ CrossRef ]
• Loukaitou-Sideris, A. Is it safe to walk here? Design and policy responses to women’s fear of victimization in public places. Research on women’s issues in transportation—Vol. 2: Technical papers. Proceedings 2005 , 35 , 102–112. [ Google Scholar ]
• Loukaitou-Sideris, A. Fear and safety in transit environments from the ‘women’s perspective. Secur. J. 2014 , 27 , 242–256. [ Google Scholar ] [ CrossRef ]
• Wang, Z.; Liu, L.; Haberman, C.; Lan, M.; Yang, B.; Zhou, H. Burglaries and entry controls in gated communities. Urban Stud. 2021 , 58 , 2920–2932. [ Google Scholar ] [ CrossRef ]
• Cervero, R.; Duncan, M. Walking, bicycling, and urban landscapes: Evidence from the San Francisco Bay area. Am. J. Public Health 2003 , 93 , 1478–1483. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Stantec. Proposed Walkability Strategy for Edmonton. Available online: https://www.edmonton.ca/public-files/assets/document?path=PDF/WalkabilityStrategy200909.pdf (accessed on 30 March 2024).
• Owen, N.; Cerin, E.; Leslie, E.; duToit, L.; Coffee, N.; Frank, L.D.; Bauman, A.E.; Hugo, G.; Saelens, B.E.; Sallis, J.F. Neighborhood walkability and the walking behavior of Australian adults. Am. J. Prev. Med. 2007 , 33 , 387–395. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cervero, R.; Kockelman, K. Travel demand and the 3Ds: Density, diversity, and design. Transp. Res. Part D Transp. Environ. 1997 , 2 , 199–219. [ Google Scholar ] [ CrossRef ]
• Almehrej, M.I. A Design Guide for Contemporary Saudi Arabian Homes in Riyadh. Ph.D. Thesis, Northumbria University, Newcastle upon Tyne, UK, 2015. [ Google Scholar ]

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Homoud, M.; Jarrar, O.M. Walkability in Riyadh: A Comprehensive Assessment and Implications for Sustainable Community—Al-Falah Case Study. Sustainability 2024 , 16 , 8073. https://doi.org/10.3390/su16188073

Homoud M, Jarrar OM. Walkability in Riyadh: A Comprehensive Assessment and Implications for Sustainable Community—Al-Falah Case Study. Sustainability . 2024; 16(18):8073. https://doi.org/10.3390/su16188073

Homoud, Majd, and Ola M. Jarrar. 2024. "Walkability in Riyadh: A Comprehensive Assessment and Implications for Sustainable Community—Al-Falah Case Study" Sustainability 16, no. 18: 8073. https://doi.org/10.3390/su16188073

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• DOI: 10.1109/CCECE59415.2024.10667327
• Corpus ID: 272604113

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