National Academies Press: OpenBook

Application of Big Data Approaches for Traffic Incident Management (2023)

Chapter: Chapter 2 - Gather Information and Data and Define Use Cases

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Suggested Citation:"Chapter 2 - Gather Information and Data and Define Use Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Application of Big Data Approaches for Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/27300.
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Suggested Citation:"Chapter 2 - Gather Information and Data and Define Use Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Application of Big Data Approaches for Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/27300.
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Suggested Citation:"Chapter 2 - Gather Information and Data and Define Use Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Application of Big Data Approaches for Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/27300.
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Suggested Citation:"Chapter 2 - Gather Information and Data and Define Use Cases." National Academies of Sciences, Engineering, and Medicine. 2023. Application of Big Data Approaches for Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/27300.
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4 This chapter describes the approach, findings, and outputs of the information-gathering task, which involved the following activities: • Conduct interviews with partners and data providers, • Collect data, and • Define initial use cases. Each of these activities is discussed in the following sections. 2.1 Conduct Interviews with Partners and Data Providers The research team arranged and conducted telephone conversations with transportation agencies and data providers. Several agencies expressed interest in participating in the research effort and providing data for the identified use cases. The organizations that participated in the interviews were • Arizona Department of Transportation, • Colorado Department of Transportation, • FHWA, • Georgia Department of Transportation, • HAAS (or Heedful Audio Alert System) Alert, • Iowa Department of Transportation, • Kentucky Transportation Cabinet, • Maryland Department of Transportation, • Minnesota Department of Transportation, • Oregon Department of Transportation, • Pennsylvania Turnpike Commission, • Tennessee Department of Transportation, • Tennessee Highway Patrol, • Transcom, • Utah Department of Transportation, • Washington State Department of Transportation, • A free navigation app provider, and • A CV data provider. The purpose of the conversations was twofold: • Identify agency pain points/challenges with respect to TIM, and • Discuss data available for sharing to support NCHRP Project 03-138. C H A P T E R 2 Gather Information and Data and Define Use Cases

Gather Information and Data and Define Use Cases 5 The outcomes as they relate to these two objectives are presented in the following subsections. 2.1.1 Agency Pain Points/Challenges The first part of the discussion with each agency focused on agency challenges with respect to TIM. Each agency was asked the following questions: • What are your agency’s biggest TIM “pain points”? • What would you like to know or be able to do that you currently do not know or cannot do? Agencies discussed a wide range of challenges and limitations. The team organized the responses into the following categorized list: • Data management. – Sharing information/data between TIM and freeway operations. (Data are not easily acces- sible, making it hard to know what was happening operationally or how the freeway per- formed during an incident.) – Organizing the data. – Using early dashboards (expensive due to the time spent on data organization). – Breaking down data silos. – Identifying integration points with different data sources. – Establishing and making use of real-time data. • Incident detection. – Understanding performance with respect to incident detection (e.g., a lot of focus is put on incident verification but not as much on incident detection). – Integrating data sources to identify unexpected slowdowns. – Improving incident detection without the addition of more cameras. – Detecting crashes in work zones. – Improving the time lag between incident detection and incident response. – Integrating CAD systems (would be beneficial for detection). – Calculating cost-benefit ratios for cameras. (It would be useful to understand where the deployment of technology would bring about the best return on investment.) • TIM timeline/performance. – Exploring clearance times, which are not easy to identify from crash reports and data from law enforcement, as they may not always be the last on the scene. – Baselining performance (would be nice to click a button to get an indication of performance in real time, e.g., detection, verification, and clearance). – Tracking arrival and departure times for law enforcement and other emergency responders. • Secondary crashes. – Overcoming the lack of data on secondary crashes. – Developing a better understanding of secondary crashes (temporal and spatial boundaries) and providing this information to law enforcement. • Responder struck-by crashes. – Creating a dashboard to determine hotspots for responder struck-by crashes. – Developing predictive models to determine propensity scores or probability for struck-by crashes (if a responder remains on the road). • Alternate routes. – Understanding how traffic behaves in identified and unidentified detour routes. – Developing a more data-driven approach for selecting/recommending alternative routes along a stretch of roadway. (Initial screening would show potential impacts, travel time, and performance on these roadways.) – Integrating arterial and freeway operations using data and technology.

6 Application of Big Data Approaches for Traffic Incident Management • Free navigation app data validation. – Identifying what additional information can be extracted from free navigation app data. – Identifying individuals that are repeatedly reporting and determining whether they can be trusted more than others. – Comparing timeliness of free navigation app data with TMC and law enforcement data. • ML and AI applications. – Conflating weather data with traffic data and crash data to predict crashes during inclement weather. – Introducing ML to extract details from the crash report narratives to feed into dashboards/ key performance indicators. – Developing queue prediction and warning systems. – Utilizing video or imagery to improve situational awareness. 2.1.2 Data Discussions with agencies surrounding the availability of data primarily included the following types or sources of data: • Advanced traffic management systems (ATMSs)/TMC, • SSP, • Crashes, • CAD/AVL, • Roadway inventory, • Linear referencing system (LRS), • Fixed roadway sensors, • Weather, • Free navigation app, and • Probe vehicle speed. While the team did not request all data sources from all agencies, for most of the data sources requested, agencies expressed both an ability and a willingness to share their data. The following are a few exceptions: • One agency indicated that it could not share third-party data. • One agency did not have a partnership with a third-party organization, and therefore it did not have access to those data. • One agency was not allowed to share crash data. • Third-party data were determined, in most cases, to be too costly to purchase for this project. • AVL data are difficult to share for privacy purposes. In a few cases, data that agencies had agreed to share were not shared due to resource con- straints on the part of the agencies. 2.2 Define Use Cases Using the input and findings from discussions with the organizations, as described in the previous section, the team developed, vetted, and finalized a list of TIM big data use cases to focus on during this project. These use cases included the following: • Use Case 1: Improving Incident Detection and Verification to Expedite Response. • Use Case 2: Real-Time TIM Timeline and Performance Measures.

Gather Information and Data and Define Use Cases 7 • Use Case 3: Understanding Secondary Crashes. • Use Case 4: Exploratory Analysis of Third-Party CV Data for Crash Detection. It should be noted that other use cases were considered (e.g., crashes in work zones, use of AVL data for clearance times, efficacy of TIM responder training and move over laws). However, such use cases were deemed to be impractical for this project (e.g., data not available, data too costly, low data quality). The research question, current practice, problem or limitation, big data opportunity, data requirements, and scope of each use case are further described in Chapter 4.

Next: Chapter 3 - Datasets and Data Quality »
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Big data is evolving and maturing rapidly, and much attention has been focused on the opportunities that big data may provide state departments of transportation (DOTs) in managing their transportation networks. Using big data could help state and local transportation officials achieve system reliability and safety goals, among others. However, challenges for DOTs include how to use the data and in what situations, such as how and when to access data, identify staff resources to prepare and maintain data, or integrate data into existing or new tools for analysis.

NCHRP Research Report 1071: Application of Big Data Approaches for Traffic Incident Management, from TRB's National Cooperative Highway Research Program, applies the guidelines presented in NCHRP Research Report 904: Leveraging Big Data to Improve Traffic Incident Management to validate the feasibility and value of the big data approach for Traffic Incident Management (TIM) among transportation and other responder agencies.

Supplemental to the report are Appendix A through Appendix P, which detail findings from traditional and big data sources for the TIM use cases; a PowerPoint presentation of the research results; and an Implementation Memo.

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