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

2025-07-31

AI Summary

High-profile highway–rail grade crossings (HRGCs) pose significant risks of vehicle undercarriage entrapment due to vertical profile irregularities. Conventional contact-based profiling methods are costly, traffic-disruptive, and hazardous. This paper proposes a non-contact, vehicle-mounted IMU/GPS–based approach for high-accuracy crossing profile reconstruction without traffic interruption. We introduce a novel hybrid LSTM–Transformer architecture—configured in both serial and parallel topologies—to enhance spatiotemporal modeling of long sequential sensor data. The model is trained end-to-end using ground-truth profiles acquired from an industrial-grade walk-behind profilometer. Field validation in Oklahoma demonstrates robust generation of 2D and 3D crossing profiles with prediction errors substantially lower than those of baseline models. The method enables rapid, safe, and scalable inspection and assessment of HRGCs, supporting proactive infrastructure management.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Large Vision Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Safety risks from hump crossings at highway-railway intersections
• Costly, slow conventional methods for HRGC profile measurement
• Need rapid, accurate hang-up susceptibility assessment for safety

Innovation

• Hybrid LSTM-Transformer deep learning framework
• IMU and GPS sensors for data collection
• 2D/3D HRGC profile generation for safety