近年来，随着智能手机App的广泛应用，一种新型的、网约式的、按需设计的需求响应式公交服务系统在全世界范围内迅速兴起。传统公交车辆调度方法不足以支撑此类新型公交系统的车辆调度。本项目围绕需求响应式公交车辆调度这一新问题，主要研究三个问题：(i)需求响应式公交系统最小车队规模问题的计算方法；(ii)需求响应式公交车辆静态调度方法；(iii)需求响应式公交车辆动态调度方法。本项目将综合运用网络流模型、逆差函数模型与车辆共享网络模型的理论知识，构建基于数学规划模型的高等逆差函数模型；设计两阶段人机交互式优化算法；并通过数值实验与案例分析对模型与算法的有效性进行验证。本项目希望借鉴人机交互式优化方法的最新研究成果，将调度员与乘客的智慧、经验与偏好等要素通过图形用户界面接口嵌入到计算机对问题进行优化求解的过程中，从而构建一套全新的、更加灵活高效的、更加符合现实所需的需求响应式公交车辆调度的新方法体系。

In recent years, with the widespread use of smartphone applications, a new and innovative mode of cyber-enabled, demand-responsive transit (DRT) systems has been springing up across the world. The vehicle scheduling methods used in conventional transit systems cannot be directly applied to solve the vehicle scheduling problem of the new DRT systems. This research project focuses on the vehicle scheduling problem of the new DRT systems, and it is three-fold: (i) developing methods for calculating the minimum fleet size required for the new DRT systems, (ii) developing static vehicle scheduling methods for the new DRT systems, and (iii) developing dynamic vehicle scheduling methods for the new DRT systems. Based on the theories of network flow models, deficit function models and vehicle-shareability network models, a new mathematical programming-based advanced deficit function model will be developed for both static and dynamic vehicle scheduling of the new DRT systems. A two-stage human-machine interactive optimization method will be developed for solving the mathematical programming-based advanced deficit function model. Numerical experiments and case studies will be conducted to test the effectiveness of the model and optimization methods developed. It is anticipated that by utilizing the latest research results of human-machine interactive optimization method, the wisdom, experience and preference of DRT schedulers and users can be incorporated into the computerized optimization process via graphical user interfaces so as to develop a completely new, more flexible, more efficient, and more practical methodology for the vehicle scheduling of the new DRT systems.