Dynamic On-Demand Vehicle Services for Small or Medium-sized Cities based on the E-CARGO Model

题目：基于E-CARGO模型的中小城市动态按需车辆服务公共交通系统是为中小城市居民提供的一项必不可少且非常昂贵的服务。像安大略北湾这样的城市需要为这项服务提供近300万美元的补贴。在日常运营中，有时只有两名、一名甚至零名乘客。这种情况表明有必要调查改善公共运输系统业务的可能性。为此，我们提出了动态按需用车服务。该服务可实时或定期对骑手请求提供快速响应。根据这些请求，交通管理局建立了按需车辆服务。这项服务旨在减少车辆的数量和尺寸，降低运营成本，提高车辆利用率，最终为居民提供更好和更经济的服务。动态的乘客-车辆分配和有效的路线规划是本研究要解决的主要挑战。这两个挑战的解决方案将影响乘客的满意度和交通系统的成本。这类问题涉及许多因素，包括许多参数。它们可以归类为多目标优化问题，并且非常复杂。最佳解决方案可能很难找到，甚至根本不可能找到。我的研究旨在克服复杂问题并提供实用的解决方案。为了实现这些目标，我们需要用一个定义良好的模型来形式化相关的问题。需要一个仿真系统来评估备选调度方法。最后，需要实用的车辆路径算法来实现基于动态乘客请求的最优车队部署。本研究的坚实理论基础来自于我过去在NSERC关于基于角色的协作（RBC）、组角色分配（GRA）和环境-类、代理、角色、组和对象（E-CARGO）模型的研究结果。模拟将建议的服务映射到RBC系统，该系统包括乘员/车辆匹配、乘员/车辆分配和车辆路由。本研究旨在提供：1)一种有效的评估算法来获取相关的人车匹配值；2)基于上述评价和所需约束条件的实用赋值算法；3)一种实用的动态路由算法；4)设立建议服务的实用参考价值；以及5)促进所建议的服务的模拟系统。本研究对这些创新服务进行了深入的调查。其结果将为中小城市节省大量公共交通成本。提出的解决方案可以应用于加拿大和世界各地类似规模的城市。

Title: Dynamic On-Demand Vehicle Services for Small or Medium-sized Cities based on the E-CARGO ModelThe public transit system is an essential and very expensive service provided to the residents of a small or medium-sized city. A city like North Bay, Ontario needs to subsidize almost $3 million to this service. During daily operations, there can be times when there are only two, one or even zero riders. This situation demonstrates a need to investigate possibilities for operational improvements to public transit systems.To this end, we propose a dynamic on-demand vehicle service. This service offers rapid responses to rider requests in real-time or at periodic intervals. Based on these requests, the transit authority sets up on-demand vehicle services. This service aims at decreasing the number of scheduled vehicles and their sizes, lowers the operation cost, increases the vehicle utilization, and finally, provides an improved and more economical service to the residents.Dynamic rider-to-vehicle assignment and efficient route planning are the major challenges to be addressed in this investigation. Solutions to these two challenges will affect the degree of rider satisfaction and the transit system's costs.Such problems involve many factors and include many parameters. They can be categorized as multi-objective optimization problems and are very complex. An optimal solution can be difficult or impossible to find. My research aims at overcoming complexity issues and offering practical solutions. To achieve these objectives, we need to formalize the related problems with a well-defined model. A simulation system is required for the evaluation of alternative scheduling methods. Finally, practical vehicle routing algorithms are needed for optimal fleet deployment based on dynamic rider requests.A solid theoretical foundation for this research comes from the results of my past NSERC research on Role-Based Collaboration (RBC), Group Role Assignment (GRA) and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. The simulation maps the proposed service into an RBC system that includes rider/vehicle matching, rider/vehicle assignment, and vehicle routing. This research is to provide: 1) an effective evaluation algorithm to acquire pertinent rider-vehicle matching values; 2) a practical assignment algorithm based on the above evaluation and required constraints; 3) a practical routing algorithm in a dynamic situation; 4) practical reference values to set up the proposed services; and 5) a simulation system for facilitating the proposed service. This research conducts a thorough investigation of these innovative services. The outcomes will offer considerable savings in public transportation costs for small or medium-sized cities. The proposed solutions can be applied to similarly sized cities throughout Canada and around the world.