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

TL;DR

This paper introduces a hybrid deep learning framework combining LSTM and Transformer models to efficiently and accurately measure highway-railway grade crossing profiles, improving safety assessments.

Contribution

A novel hybrid LSTM-Transformer deep learning approach for cost-effective and rapid HRGC profile measurement using field instrumentation data.

Findings

Models 2 and 3 outperformed others in efficiency and accuracy.

The models successfully generated 2D/3D HRGC profiles.

Deep learning models enhanced safety assessment capabilities.

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.