Traditionally, agencies have collected data on traffic incidents and TIM activities in a variety of ways, including crash reports, advanced traffic management systems (ATMS), computer-aided dispatch (CAD) systems, safety service patrol (SSP) programs, and traffic citation systems. As such, data on traffic incidents exist; however, much of the data are not available and ready for use in analyses. Therefore, agencies and TIM programs infrequently use these data to enhance their understanding of how they could improve TIM practices and policies to reduce system impacts of traffic incidents. There are three primary challenges that contribute to this limitation in traffic incident data: 

  • Lack of data sharing – The multi-disciplinary nature of TIM creates challenges for data availability. Transportation agencies are responsible for managing transportation system performance; yet partner agencies, including law enforcement, fire and rescue, emergency medical services (EMS), and towing, are often involved in responding to and clearing traffic incident scenes. These partners collect data on their response activities via their own methods and systems. Sharing data between agencies is relatively new and limited due to factors including disparate data systems, sensitive data, and agency culture. 
  • Data quality issues – Traditional sources of traffic incident data are collected manually by humans, which can contribute to data quality issues such as missing data and erroneous data. Manual data collection can also lead to data inconsistencies (e.g., where free text is allowed). Timeliness of traffic incident data is also an issue. Data made available months or years after collection have less value than data made available immediately after collection. Finally, because data are collected and stored in silos, they are often difficult to integrate as they lack a common unique identifier.
  • Traditional data management – Traditionally, transportation agencies, as well as TIM partner agencies, have managed internal data in silos using various tools including spreadsheets and relational database systems. More recently, some agencies have begun to look beyond their traditional sources of incident data to emerging data sources, such as navigation systems data, crowdsourced data, and probe vehicle data, to better understand the impacts of traffic incidents on transportations system performance and TIM performance. These data can be voluminous and structured in a way that does not fit well with an agency’s traditional data management systems.

To maximize the potential for data to improve TIM and to reduce the transportation system impacts of traffic incidents, agencies must improve the sharing, quality, and management of traffic incident data. The purpose of this guide is to provide lessons learned and recommendations for transportation agencies on improving the sharing, quality, and management of data for TIM use cases. The guide contains four sections:

  • Section 1. TIM Data Sharing – describes a wide range of TIM relevant data, presents shared challenges and limitations in data sharing, provides examples of successful data sharing in TIM and the benefits associated with sharing and gaining access to data from internal DOT groups, external TIM partner agencies, and private data providers in support of a range of TIM use cases. 
  • Section 2. TIM Data Quality – presents the findings from comprehensive assessments of TIM data quality, including the quality issues and limitations with certain datasets, and offers recommendations for agencies to improve data quality.
  • Section 3. TIM Data Management – summarizes the most common data management challenges and associated limitations and provides recommendations and guidelines for modern data management from recent research. 
  • Section 4. Opportunities – discusses opportunities for TIM agencies to accelerate the collection, sharing, and use of data to improve TIM practices and policies, performance, and the overall impacts of traffic incidents on transportation networks.

This guide should help agencies better understand the limitations of the data, the benefits of change, and what steps they can make to improve the data to support TIM use cases.

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