Artificial intelligence aided detection and analysis of vehicular stop on track at railroad grade crossings
Year: 2025
Type: article
Abstract: Purpose This paper aims to address this rail safety issue through collection and analysis of video data at grade crossings using artificial intelligence. Railroad safety is vital to transportation and the economy, particularly in the USA, where highway-rail grade crossings are hotspots of accidents, 9% of which involve vehicles stopping on tracks. Design/methodology/approach This research develops a robust artificial intelligence (AI)-assisted system to detect stopped-on-tracks incidents by overcoming unique challenges in the scene of grade crossings via neural architecture search and dynamic trajectory filtering. Findings The system achieves 94% precision in stopped-on-tracks detection and 95% recall in traffic counting. The findings suggested that road markings in the first case study facilitate incident reduction up to 50%, while a targeted intervention could potentially decrease stopped-on-tracks incidents by 80% in the second case study. Social implications This AI-based approach will potentially enhance rail safety and enable more informed decision-making in rail infrastructure management by providing insight on stopped-on-tracks behavior. Originality/value The model’s accuracy was evaluated using the test set. Its effectiveness was further validated through two case studies: one assessing the impact of road markings (e.g. dynamic envelopes) on reducing stopped-on-tracks incidents and another examining the correlation between different types of traffic congestion and these incidents.
Source: Smart and Resilient Transport
Authors: Huixiong Qin, Asim Francis Zaman, Sarthak Jain, Xin Wang, Xiang Liu
Institution: Rutgers, The State University of New Jersey
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7108204188