Automated Assessment of Safety-Critical Dynamics in Multi-modal Transportation Systems

With the advent of emerging technologies, urban intersections are being increasingly equipped with various types of video-based and in-pavement sensing systems to facilitate round-the-clock monitoring and optimization of multi-modal flows. In comparison, the assessment of the safety performance of these facilities continues to be largely based on either crash history or citizen grievances. Herein lies an opportunity to apply advanced sensing platforms to proactively monitor safety-critical events of multi-modal road users. This work presents a traffic safety monitoring framework which showcases the capabilities of utilizingin-pavement sensors to provide a detailed, automated assessment of mobility and safety-related performance measures for multi-modal traffic at signalized intersections. This effort supplements the crash data-based retrospective studies by routinely monitoring the safety-critical behavior of multimodal traffic. Herein, the term safety-critical refers to any action or interaction that can adversely impact a road user’s safety, including jaywalking, red-light running, and drivers not yielding to pedestrians. Unlike in-person surveys and video analysis techniques which are limited in their scope to short term study periods, in-pavement sensors facilitate round-the-clock, non-intrusive data collection over continuous periods of time. Consequently, the development of suitable automated algorithms to analyze this data can generate a report of safety-critical multi-modal dynamics. Such an output can be used by agencies to proactively identify, and address, hazardous locations before a crash occurs.