Hybrid LSTM-Transformer Models for Profiling Highway-Railway Grade Crossings

• URL: http://arxiv.org/abs/2508.00039v1
• Date: Thu, 31 Jul 2025 06:44:44 GMT
• Title: Hybrid LSTM-Transformer Models for Profiling Highway-Railway Grade Crossings
• Authors: Kaustav Chatterjee, Joshua Q. Li, Fatemeh Ansari, Masud Rana Munna, Kundan Parajulee, Jared Schwennesen,
• Abstract summary: High-profile Highway Railway Grade Crossings (HRGCs) pose safety risks to highway vehicles due to potential hang-ups.<n>Conventional methods for measuring HRGC profiles are costly, time-consuming, traffic-disruptive, and present safety challenges.<n>This research employed advanced, cost-effective techniques and innovative modeling approaches for HRGC profile measurement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hump crossings, or high-profile Highway Railway Grade Crossings (HRGCs), pose safety risks to highway vehicles due to potential hang-ups. These crossings typically result from post-construction railway track maintenance activities or non-compliance with design guidelines for HRGC vertical alignments. Conventional methods for measuring HRGC profiles are costly, time-consuming, traffic-disruptive, and present safety challenges. To address these issues, this research employed advanced, cost-effective techniques and innovative modeling approaches for HRGC profile measurement. A novel hybrid deep learning framework combining Long Short-Term Memory (LSTM) and Transformer architectures was developed by utilizing instrumentation and ground truth data. Instrumentation data were gathered using a highway testing vehicle equipped with Inertial Measurement Unit (IMU) and Global Positioning System (GPS) sensors, while ground truth data were obtained via an industrial-standard walking profiler. Field data was collected at the Red Rock Railroad Corridor in Oklahoma. Three advanced deep learning models Transformer-LSTM sequential (model 1), LSTM-Transformer sequential (model 2), and LSTM-Transformer parallel (model 3) were evaluated to identify the most efficient architecture. Models 2 and 3 outperformed the others and were deployed to generate 2D/3D HRGC profiles. The deep learning models demonstrated significant potential to enhance highway and railroad safety by enabling rapid and accurate assessment of HRGC hang-up susceptibility.

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