Liu, Ruifeng (University of Massachusetts Lowell), Xie, Yuanchang (University of Massachusetts Lowell), Stamatiadis, Polichronis (University of Massachusetts Lowell), Gartner, Nathan (University of Massachusetts Lowell), Ge, Tingjian (University of Massachusetts Lowell)

Enhancing Traffic Incident Detection through ADASYN-Attention Fusion: A Comparative Study with RITIS Data

Scheduled for presentation during the Poster Session "Incident and emergency management" (WeBT17), Wednesday, September 25, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

Traffic incidents are a leading contributor to non-recurring congestion and secondary crashes. Each year congestion and crashes together cost the United States over 1 trillion dollars. Once traffic queues are formed, it is difficult to dissipate them and return traffic to normal operations. Therefore, real-time and accurate incident detection plays a critical role in Traffic Incident Management (TIM). This research focuses on highway traffic incident detection. It divides a highway network into short segments and correlates temporal and spatial data from adjacent segments for detecting incidents. Due to incidents being relatively rare compared to normal traffic patterns, we propose a method that combines oversampling with the attention mechanism and use an ablation study to prove its effectiveness in improving supervised incident detection.