Citation

Pan D, Hamdar S. Transp. Res. Rec. 2024; 2678(2): 389-400.

Copyright

(Copyright © 2024, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981231174445

PMID

unavailable

Abstract

The time and location of occurrence, among other characteristics of incidents, are needed to distribute traveler information and dispatch emergency service for traffic congestion and safety mitigation purposes. However, the corresponding clearance/recovery time is an important, yet under-studied, subject of research. Existing studies on incident duration modeling mainly utilize duration data gathered and provided by authorities through the efforts of individual agents. This type of manual data gathering may suffer coverage and consistency issues. In addition, the explanatory variables considered in these studies are static, that is, observed at the incident formation stage and assumed to be consistent over the incident periods. This setup, however, overlooks traffic flow dynamics; time-varying traffic variables during the incident episodes affect incidents clearance and disruption characteristics. In line with the above limitations, the objective of this study is to utilize hazard-based modeling to explain incident durations mined from traffic detector data while factoring in traffic flow dynamics. The 2014-2016 Virginia statewide traffic detector data were utilized to extract incident durations by implementing a machine-learning-based incident detection algorithm followed by a semi-parametric proportional hazard function. Such a function accounts for the time-varying traffic-descriptive covariates for incident duration modeling. The resulting monotonically decreasing baseline hazard reveals the snowball effect and the inertia effect of disruptions induced by incidents, which leads to the advice that incidents should be detected and mitigated within a 30 min duration. The normalized flow, density, and speed measures, and their temporal differences are all shown to be significant covariates scaling up or down the hazard.

Language: en