Crash-based safety evaluation is often hampered by randomness, lack of timeliness, and rarity of crash occurrences. This is particularly the case for technology-driven safety improvement projects that are frequently updated or replaced by newer ones before it is possible to gather adequate crash data for a reliable and defensible before-after evaluation. Surrogate safety data are commonly used as an alternative to crash data; however, its current practice is still resource intensive and subject to human errors. The advent of connected vehicle technology allows vehicles to communicate with each other and infrastructure wirelessly. This platform also offers the opportunity for automated and continuous tracking of vehicle trajectories and signal status at the facilities in real time. These types of data can potentially be extracted and used to detect the deficiencies in the safety performance of the facility operation. This project examines the viability of long-term monitoring of connected vehicle data for safety performance evaluation. As limited saturation of onboard equipment (OBE) is expected in the near-term evolution, the study focuses on a connected vehicle application that can process data elements from OBEs via vehicle-to-infrastructure communications using standard message sets. To accomplish the objective, a signalized intersection test bed was created in VISSIM while the wireless communications capability and the application were implemented using Car-to-Everything Application Programming Interface. The evaluation results indicated that the application can effectively detect changes in safety performance at full market penetration.


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    Title :

    Next Generation Safety Performance Monitoring at Signalized Intersections Using Connected Vehicle Technology


    Contributors:
    L. Zha (author) / P. Songchitruksa (author) / K. N. Balke (author)

    Publication date :

    2014


    Size :

    72 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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