用AI和激光扫描评估高铁平交道口挂碍风险，助力交通部门防事故。
Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future
- 融合LSTM与Transformer模型，从激光数据重建道口剖面。
- 三种车辆尺寸下分别有70、80、95个道口处于高挂碍风险。
- 生成可交互的地理数据库，供交通部门快速识别隐患点。
陡坡型高速公路铁路平交道口（HRGCs）对离地间隙较低的车辆构成安全威胁，可能导致车辆卡在轨道上，引发列车碰撞。本研究构建了网络级评估HRGCs挂碍风险的框架。通过步行式剖面仪和Pave3D8K激光成像系统采集俄克拉荷马州多个道口的剖面数据，开发了一种结合长短期记忆（LSTM）与Transformer架构的混合深度学习模型，用于从Pave3D8K数据中精确重建HRGC剖面。收集了约350辆特种车辆在俄克拉荷马州多地的车辆尺寸数据，以获取最新的统计设计参数。基于三种车辆尺寸情景分析挂碍风险：(a) 中位数尺寸（中位轮距与离地间隙），(b) 75-25百分位尺寸（75%轮距，25%离地间隙），(c) 最差情况尺寸（最大轮距，最小离地间隙）。结果显示，三种情景下分别有70、80、95个道口处于最高风险等级。开发了基于ArcGIS的数据库及软件界面，支持交通管理部门进行道口隐患治理。该框架通过整合新一代传感技术、深度学习与基础设施数据，推动安全评估向实用决策工具演进。
原文摘要 · Abstract (English)
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.
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