Optimization of on-demand transportation provisions and ride-sharing services in evacuations may provide increased network capacity and enhanced evacuation performance to transportation systems and improve equity and disaster preparedness for community and society. This paper proposes a two-phase model for optimizing trip planning and operations by integrating a ride-sharing process in short-notice evacuations, to allow a joint optimization of driver-rider matching and necessary transfer connections among shared vehicle trips. In the first phase, following network topology information and personal requests, a vehicle-space-time hyper dimensional network is developed by constructing vehicle-space-time vertexes and arcs. In the second phase, based on the constructed vehicle-space-time network, a new time-discretized multi-rider multi-driver network flow model is built to formulate ride-sharing with connecting transfers. A Lagrangian relaxation solution approach is designed to solve the model in a real-world network scenario. Numerical analyses are conducted with considerations given to the three operating parameters (detour tolerance of driver, penalty factor for transfer time, and maximum allowable parking time) in the method, and the analysis results show that the proposed model can not only meet the evacuation trip needs of the participating parties but it also supports personalized requests and on-demand accesses. A small sample network is used to theoretically test the whole model and the underlying concepts and solution strategy to show each step implemented in details, and finally the applicability of the method is demonstrated using the Chicago City network.

在疏散中优化按需运输条款和共享乘车服务可以提高运输系统的网络容量和疏散性能，并改善社区和社会的公平性和备灾能力。本文提出了一个两阶段模型，该模型通过在短通知疏散中整合乘车共享过程来优化行程计划和运营，以实现驾驶员与驾驶员的匹配以及共享车辆行程之间必要的换乘连接的联合优化。在第一阶段中，根据网络拓扑信息和个人请求，通过构造车时空顶点和弧线来开发车时空超维网络。在第二阶段，基于构建的车时空网络，建立了一个新的时间分散的多驾驶员多驾驶员网络流量模型，以建立具有连接转移的乘车共享。拉格朗日松弛解决方案方法旨在解决现实网络场景中的模型。该方法在考虑了三个操作参数（驾驶员的tour回公差，转移时间的惩罚因子和最大允许停车时间）的基础上进行了数值分析，分析结果表明所提出的模型不仅能够满足疏散行程参与方的需求，但它也支持个性化请求和按需访问。一个小的样本网络用于理论上测试整个模型以及基本概念和解决方案策略，以详细展示每个步骤的实现，