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[2512.12832] Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future
Summary
This research paper evaluates the hangup susceptibility of Highway Railway Grade Crossings (HRGCs) using deep learning and sensing techniques to enhance safety for vehicles with low ground clearance.
Why It Matters
The study addresses a critical safety issue at HRGCs, where vehicles can become stranded, leading to potential train collisions. By utilizing advanced deep learning models and comprehensive data collection, this research aims to provide actionable insights for transportation agencies to mitigate these risks, ultimately contributing to safer infrastructure.
Key Takeaways
- Developed a hybrid deep learning model to assess HRGC safety.
- Identified high-risk crossings based on vehicle dimension scenarios.
- Created an ArcGIS database to assist transportation agencies in hazard mitigation.
Computer Science > Machine Learning arXiv:2512.12832 (cs) [Submitted on 14 Dec 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future Authors:Kaustav Chatterjee, Joshua Li, Kundan Parajulee, Jared Schwennesen View a PDF of the paper titled Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future, by Kaustav Chatterjee and 3 other authors View PDF Abstract:Steep-profiled Highway Railway Grade Crossings (HRGCs) pose safety hazards to vehicles with low ground clearance, which may become stranded on the tracks, creating risks of train vehicle collisions. This research develops a framework for network level evaluation of hang-up susceptibility of HRGCs. Profile data from different crossings in Oklahoma were collected using both a walking profiler and the Pave3D8K Laser Imaging System. A hybrid deep learning model, combining Long Short Term Memory (LSTM) and Transformer architectures, was developed to reconstruct accurate HRGC profiles from Pave3D8K Laser Imaging System data. Vehicle dimension data from around 350 specialty vehicles were collected at various locations across Oklahoma to enable up-to-date statistical design dimensions. Hang-up susceptibility was analyzed using three vehicle dimension scenarios: (a) median dimension (median wheelbase and ground clearance),...
