Work

An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings

Year: 2025

Type: article

Abstract: Previous studies have addressed the pedestrian-vehicle conflicts at Rectangular Rapid Flashing Beacons (RRFB) signals. However, none have explored vehicle-to-vehicle (v2v) conflicts, leaving a significant gap in the current research. This study fills this gap by analyzing v2v rear-end conflicts at RRFB signals. Fifty-two hours of video data were collected using portable CCTV cameras and analyzed using computer vision algorithms. A bounding box system was employed to predict vehicle conflict points and collision pairs. Modified Time to Collision (MTTC) and Deceleration Rate to Avoid Collision (DRAC) were taken individually as conflict indicators, and peak-over-threshold method was employed to determine the threshold for conflict analysis. The comprehensive feature selection process ensures the identification of the most significant predictors. The average speed of vehicles, standard deviation of speed, and traffic volume in each lane, in addition to time of day, signal activation, pedestrian count, and pedestrian signal compliance rate were identified as important features. Initially, the Time-Embedded Transformer model was applied separately to MTTC and DRAC. The thresholds of ≤1.5 seconds as conflict for MTTC and ≥4 m/s2 as conflict for DRAC produced the best results, achieving recall 82% and 83%, and False Alarm Rate (FAR) 27% and 38%, respectively. For improved performance, these models were then combined using a voting classifier technique. This combination led to significant improvements, achieving a recall 86% and FAR 23%. Insights from the study can optimize RRFB performance by identifying conflict times and locations, improving driver alerts during peak hours, and ensuring smoother traffic flow and reliability.

Source: Transportation Engineering

DOI: 10.1016/j.treng.2025.100318

Authors: Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

Institution: University of Central Florida

Language: English

Topic: Traffic and Road Safety

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,670