Connected Vehicle Data in Systems Analysis of Roadway Safety and Access Management

Uncertain and evolving conditions on several time horizons can jeopardize the safety and mobility of goods and people on road networks. While sensor vehicle data aids in assessing system safety, uncertainties remain. New connected-vehicle (CV) technology offers increasingly higher resolutions of data for evaluating potential safety counter-measures. This paper develops a systemic risk and multi-criteria analysis of roadway network performance, utilizing CV data to inform parametric models. Its innovation is the use of CV data in a particular type of risk assessment, where risk is quantified as the disruption of priority orders of locations across a region. An included demonstration uses newly available connected vehicle movement and event data of a transportation agency. The results guide the implementation of safety protocols, prioritizing access management at locations of vulnerable transport networks. The paper should be of interest to stakeholders that include planners, engineers and traffic management authorities. The approach and insights are relevant to the monitoring for anomalies across a variety of systems and networks. In our analysis, the study identified significant variability in roadway performance, with mornings and weekends and peak hours showing the highest disruptions scores of 7, 6, and 6 respectively. The analysis revealed five segments (10, 11, 18, 19, 1) with robust performance, underscoring the importance of implementing access management techniques on these segments. These findings highlight mornings and weekends as critical times for targeted interventions to apply access management.