Abolfazl Karimpour, SUNY Polytechnic Institute – Red-Light Running Behavior at Signalized Intersections

On SUNY Polytechnic Institute Week: Why do people run red lights?

Abolfazl Karimpour, assistant professor in the engineering department, examines the data.

Dr. Karimpour is an Assistant Professor at the State University of New York Polytechnic Institute. Previously, he held positions as Manager and Assistant Research Professor at the University of Arizona’s Center for Applied Transportation Science. His research interests include Traffic Operation and Safety, Traffic Signal Optimization, Data Analytics, Public Transportation, and Smart Cities Transportation. He has published over 15 peer-reviewed research articles in various journals and presented his findings at over 20 national and international conferences. He is a Co-PI for numerous national, regional, and local transportation projects. Dr. Karimpour is an active member of TRB, ASCE, and ITE. He is a member of the TRB standing committee on Artificial Intelligence and Advanced Computing Applications (AED50). Also a young member of the Street and Highway Operations and an Associate member of the Transportation Safety of the American Society of Civil Engineers committees. Dr. Karimpour also serves as a panelist on NCHRP 17-100: Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis. He has e received several prestigious awards, inlcuidng ITE Western District outstanding graduate student award, Jenny L. Grote Student Leadership Award, and Southern Arizona ITE Hank Warner Scholarship.

Red-Light Running Behavior at Signalized Intersections

Red-light running behavior is one of the riskiest behaviors at signalized intersections and is becoming a prominent cause of intersection-related crashes. According to the report published by AAA Foundation for Traffic Safety, more than two people are being killed daily due to noncompliance with red signal indications. In addition, the crash fact report published by National Highway Traffic Safety Administration shows that red-light runners cause hundreds of fatal crashes annually.

This study proposes a novel approach for estimating RLR at signalized intersections with greater accuracy than conventional methods. The method utilizes a mixture modeling technique and leverages real-world data from the Advanced Transportation Management system installed, focusing on factors that characterize RLR behavior.

The results suggest that drivers who experience longer wait times at intersections or fail to pass through during the first cycle are more likely to run red lights. Furthermore, it was observed that larger intersections with a greater number of approach lanes exhibit a higher incidence of red-light running.

The calibrated models developed in this study can provide valuable insights into the influence of different intersection- and corridor-based characteristics on the frequency of RLR. By leveraging these models, transportation agencies can gain a deeper understanding of the factors contributing to RLR behavior in various settings, enabling them to make informed decisions and implement targeted measures to mitigate RLR risks effectively. This approach can ultimately lead to improved safety and efficiency at signalized intersections across different regions.

Read More:
[Taylor & Francis Online] – Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach