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), (b) 75-25 percentile dimension (75 percentile wheelbase, 25 percentile ground clearance), and (c) worst case dimension (maximum wheelbase and minimum ground clearance). Results indicate 70, 80, and 95 crossings at the highest hang-up risk levels under these scenarios, respectively. An ArcGIS database and a software interface were developed to support transportation agencies in mitigating crossing hazards. This framework advances safety evaluation by integrating next-generation sensing, deep learning, and infrastructure datasets into practical decision support tools.

翻译：陡峭坡度的公路铁路平交道口对低底盘车辆构成安全隐患，此类车辆可能卡滞在轨道上，从而引发列车与车辆碰撞的风险。本研究开发了一套用于网络级评估平交道口悬空风险的框架。通过步行式断面仪与Pave3D8K激光成像系统，采集了俄克拉荷马州不同道口的纵断面数据。研究构建了一种结合长短期记忆网络与Transformer架构的混合深度学习模型，用于从Pave3D8K激光成像系统数据中重建精确的平交道口纵断面。通过在俄克拉荷马州多地采集约350辆特种车辆的尺寸数据，获得了最新的统计设计维度。悬空风险分析采用三种车辆维度场景：(a) 中位维度（轴距与离地间隙中位数），(b) 75-25百分位维度（75百分位轴距与25百分位离地间隙），(c) 最不利维度（最大轴距与最小离地间隙）。结果显示，在上述场景下分别有70、80和95处道口处于最高悬空风险等级。研究开发了ArcGIS数据库与软件交互界面，以支持交通管理部门缓解道口安全隐患。该框架通过将新一代传感技术、深度学习与基础设施数据集整合为实用决策支持工具，推动了安全评估方法的进步。