This study develops a novel framework to formalize the optimal utilization of all available modes of transportation, particularly transit and school buses for a mass evacuation. The study develops an “All-Mode Evacuation Decision Support Tool (AMEDST)” to determine an optimum auto-bus composition that yields an improvement in evacuation time and network congestion. The study follows a Knapsack optimization and adopts a solution algorithm called Dynamic Programming within a Python platform to optimally allocate buses to evacuees exposed to higher level of vulnerabilities. A traffic microsimulation model follows a dynamic traffic assignment process to simulate evacuation scenarios using all available modes. Results from the traffic simulation yield a vehicular traffic reduction of 3.9–7.7% and an evacuation time reduction of 9–22.7% if 5–20% of auto-based demand are served by buses. The tool will help emergency personnel evaluate alternative scenarios for making informed decisions regarding resource allocation and emergency budget policies for large-scale evacuations.

本研究开发了一个新的框架来规范所有可用交通方式的最佳利用，特别是用于大规模疏散的公交和校车。该研究开发了一种“全模式疏散决策支持工具 (AMEDST)”，以确定能够缩短疏散时间和减少网络拥堵的最佳自动巴士组合。该研究遵循背包优化，并在 Python 平台内采用一种称为动态编程的解决方案算法，以最佳方式将公共汽车分配给暴露于更高级别漏洞的撤离人员。交通微观模拟模型遵循动态交通分配过程，以使用所有可用模式模拟疏散场景。如果 5-20% 的汽车需求由公共汽车提供，交通模拟的结果会导致车辆交通量减少 3.9-7.7%，疏散时间减少 9-22.7%。