Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, China; Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, USA.
Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, USA.
Accid Anal Prev. 2022 Apr;168:106594. doi: 10.1016/j.aap.2022.106594. Epub 2022 Feb 14.
The railroad industry plays a principal role in the transportation infrastructure and economic prosperity of the United States, and safety is of the utmost importance. Trespassing is the leading cause of rail-related fatalities and there has been little progress in reducing the trespassing frequency and deaths for the past ten years in the United States. Although the widespread deployment of surveillance cameras and vast amounts of video data in the railroad industry make witnessing these events achievable, it requires enormous labor-hours to monitor real-time videos or archival video data. To address this challenge and leverage this big data, this study develops a robust Artificial Intelligence (AI)-aided framework for the automatic detection of trespassing events. This deep learning-based tool automatically detects trespassing events, differentiates types of violators, generates video clips, and documents basic information of the trespassing events into one dataset. This study aims to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of video surveillance infrastructure through the risk analysis of their data feeds in specific locations. In the case study, the AI has analyzed over 1,600 h of archival video footage and detected around 3,000 trespassing events from one grade crossing in New Jersey. The data generated from these big video data will potentially help understand human factors in railroad safety research and contribute to specific trespassing proactive safety risk management initiatives and improve the safety of the train crew, rail passengers, and road users through engineering, education, and enforcement solutions to trespassing.
铁路行业在美国的交通基础设施和经济繁荣中发挥着重要作用，安全性至关重要。在美国，过去十年中，违规闯入是导致与铁路相关的人员死亡的主要原因，而违规闯入的频率和死亡人数几乎没有减少。尽管铁路行业广泛部署了监控摄像机和大量视频数据，使得见证这些事件成为可能，但实时视频或档案视频数据的监控需要大量的工时。为了解决这一挑战并利用这些大数据，本研究开发了一个强大的人工智能（AI）辅助框架，用于自动检测违规闯入事件。这个基于深度学习的工具可以自动检测违规闯入事件，区分违规者的类型，生成视频剪辑，并将违规事件的基本信息记录到一个数据集。本研究旨在为铁路行业提供最先进的人工智能工具，通过对特定地点的数据馈送进行风险分析，利用其视频监控基础设施的未开发潜力。在案例研究中，该 AI 分析了超过 1600 小时的档案视频片段，并从新泽西州的一个道口检测到了大约 3000 起违规闯入事件。从这些大数据中生成的数据将有助于理解铁路安全研究中的人为因素，并有助于特定的违规闯入主动安全风险管理计划，通过工程、教育和执法措施来提高火车工作人员、铁路乘客和道路使用者的安全性，以解决违规闯入问题。
