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

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 postconstruction railway track maintenance activities or noncompliance with design guidelines for HRGC vertical alignments. Conventional methods for measuring HRGC profiles are costly, time-consuming, and 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 an inertial measurement unit and global positioning system sensors, while ground-truth data were obtained via an industrial-standard walking profiler. Field data were 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 two-dimensional/three-dimensional 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.