Transit Safety System Evaluation and Hotspot Identification Empowered by Edge Computing Transit Event Logging System

Document Type

Journal Article

Publication Date

2024

Subject Area

place - north america, mode - bus, planning - safety/accidents, planning - methods, ridership - drivers, ridership - behaviour

Keywords

data and data science, intelligent transportation systems, public transportation

Abstract

This paper discusses the importance of near-crash events and associated metadata as valuable sources for smart transit applications, such as surrogate safety measures for transit safety research. The STAR Lab at the University of Washington, sponsored by the Federal Transit Administration, has developed an edge computing system that processes onboard videos for near-crash detection. This paper builds on previous work by addressing two research questions: first, how to leverage the near-crash detection system to synthesize rich data sources on transit vehicles, and second, how to use the smart data hub to support transit operation and safety studies. The proposed procedures for event-based transit data collection, evaluation of commercial collision avoidance warning systems (CAWS) technologies, and transit safety hotspot identification are detailed. CAWS’ performance was benchmarked on four transit buses that were operated for almost a year in Pierce County, WA, U.S. Furthermore, the meta-information of near-crash events enables hotspot analysis and the identification of several exemplar clusters that can be explained by driver behavior and roadway geometries. The results of the experiments demonstrate the system’s promising performance and its applicability to addressing various transit operation questions.

Rights

Permission to publish the abstract has been given by SAGE © National Academy of Sciences: Transportation Research Board 2023.

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