Coupled Situational Awareness System to Improve Transportation Infrastructure Performance during Extreme Events

The dense road networks and numerous low water crossings throughout Texas may be contributing to the higher recurrence rates of floods that pose a danger to vehicles. A timely issue that should be addressed by researchers is the compounding of disaster. Flooding can be combined with other life-threatening occurrences such as power loss and interruptions of health and emergency services. During these events, rescue requests from the stranded communities overwhelm the emergency response facilities; impassable roadways and the paucity of reliable information on the affected areas and their accessibility hamper emergency response operations, causing several detours and delays that put both the responders and evacuees at risk. This research presents a framework for improved situational awareness during extreme flooding events by combing a flood inundation model with transportation infrastructure performance assessment. The flood inundation model can be driven by real-time radar rainfall data in an efficient manner. The road network work model can use land use, census data, and locations of critical facilities in combination with spatial analysis. The proposed framework is demonstrated on a small catchment in San Antonio, Texas. The study includes the following tasks: literature review, vehicle-related flood fatality analysis, review of flood warning systems in Texas, and the proposed framework of a road flooding forecasting system that can predict land surface flooding in detail and the impacts on the road network and provide information on the spatial and temporal evolution of road network access during flooding events. It is recommended that the framework be used for identifying the transportation network-wide impacts of flood ‘hot-spots’ and assessment of transportation-related flood mitigation alternatives. The framework can also support disaster planning and emergency preparedness measures in preparation for major events. Examples may include contingency planning for deployment of barricades, mitigation of critical facilities, and large-scale evacuation planning. The methodology can provide useful outputs on system-wide costs of flooded roads that can be used to inform regional mitigation efforts.得克萨斯州境内密布的道路网络与大量低水位过水通道，或为洪水复发率偏高的诱因，此类洪水会对车辆构成安全威胁。研究人员亟待解决的一项紧迫议题，是灾害的复合效应：洪水可与断电、医疗与应急服务中断等其他致命性事件并发。在此类事件期间，受困社区的救援请求会令应急响应机构不堪重负；通行受阻的道路、受影响区域及其通行性的可靠信息匮乏，都会阻碍应急救援行动，引发大量绕行与延误，进而将救援人员与疏散人员均置于风险境地。 本研究提出一套可提升极端洪水事件期间态势感知能力的框架，该框架将洪水淹没模型（flood inundation model）与交通基础设施性能评估（transportation infrastructure performance assessment）相结合。其中，洪水淹没模型可依托实时雷达降雨数据（radar rainfall data）高效运行；道路网络模型可结合土地利用、人口普查数据（census data）与关键设施（critical facilities）位置，并辅以空间分析（spatial analysis）手段。 所提出的框架已在得克萨斯州圣安东尼奥的一处小型集水区（catchment）中得到验证。本研究涵盖以下任务：文献综述（literature review）、车辆相关洪水致死率分析（vehicle-related flood fatality analysis）、得克萨斯州洪水预警系统（flood warning systems）梳理，以及所提出的道路洪水预报系统（road flooding forecasting system）框架——该系统可详细预测地表洪水情况及其对道路网络的影响，并提供洪水事件期间道路网络通行性的时空演变（spatial and temporal evolution）信息。 建议将该框架用于识别洪水热点区域（flood hot-spots）对全交通网络（transportation network-wide）的影响，以及评估与交通相关的减灾方案（mitigation alternatives）。该框架还可支持重大灾害事件前的灾害规划与应急准备工作，例如部署路障（barricades）的应急规划（contingency planning）、关键设施减灾，以及大规模疏散规划（evacuation planning）。本研究方法可输出受淹道路的全系统成本相关有效结果，用于为区域减灾工作提供决策依据。