Leveraging High-Resolution Connected Vehicle Data for Incident Detection,Management,and Analysis: An Exploratory Study

Abstract

Transportation system managers have, for many years, depended primarily on data from fixed sensors for traffic management and analysis. Recently, however, thanks to advancements in communications technologies, global positioning systems (GPS), and on-board computers, there has been an increased interest in leveraging high-resolution connected vehicle (CV) data for such applications. The purpose of this study was to conduct a detailed analysis of the benefits that high-resolution CV data offer, relative to traditional traffic data available from fixed-location traffic sensors. This is researched within the context of incident detection, management and analysis applications, by examining three major incidents that took place on the interstate system in the Buffalo, NY metro area. Five applications of CV data to incident analysis and management were considered: 1) detecting the exact time of incident occurrence, 2) identifying the time of highway closure and re-opening, 3) quantifying queue formation and dissipation, 4) conducting detailed analysis of the impact of the incident on the freeway and the adjoining surface street network, and 5) analyzing driver behavior around the incident scene. Among the main conclusions of the study is that leveraging high-resolution CV data allow for 1) detecting incidents much earlier than traditional intelligent transportation system (ITS) technologies, 2) detecting the time of highway closure and re-opening, 3) quantifying queue formation and dissipation, 4) assessing the impact of an incident on the surrounding surface street network, and 5) analyzing driver behavior at incident scenes.