论文标题
Passgoodpool：联合乘客和商品车队管理与加强学习协助定价，匹配和路线计划
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning
论文作者
论文摘要
乘客和商品交付的需求服务的无处不在增长，在运输系统领域带来了各种挑战和机遇。结果，正在开发智能运输系统，以最大程度地提高运营盈利能力，用户便利性和环境可持续性。最后一英里交付的增长与乘车共享呼吁建立一个运送乘客和货物的高效和凝聚力系统。现有方法使用静态路由方法解决了这一问题，即在路线规划期间既不考虑请求的需求也不是车辆之间的货物转移。在本文中，我们提出了一个动态和需求意识的车队管理框架，用于联合商品和乘客运输，（1）通过允许驾驶员以相互合适的价格协商乘客和驾驶员参与决策过程，以及乘客可以接受/拒绝，以接受/拒绝，（2）货物匹配的乘以乘坐群体，以及乘坐多种途径，（3）的途径（3），（3）动态（3），（3），（3）（3），（3）（3），（3）途径（3），（3），3）然后，插入成本决定匹配的成本，（4）使用深入的强化学习（RL）将闲置车辆派遣到预期的高乘客和货物需求的领域，（5）允许在每辆车上进行分配推理，同时集体优化机队目标。我们提出的模型可以独立地在每辆车内部部署，因为这可以最大程度地减少与分布式系统的增长相关的计算成本，并使每个人的决策民主化。与不考虑合并负载运输或动态多跳路线计划的其他方法相比，对各种车辆类型，商品和乘客公用事业功能的仿真显示了我们方法的有效性。
The ubiquitous growth of mobility-on-demand services for passenger and goods delivery has brought various challenges and opportunities within the realm of transportation systems. As a result, intelligent transportation systems are being developed to maximize operational profitability, user convenience, and environmental sustainability. The growth of last mile deliveries alongside ridesharing calls for an efficient and cohesive system that transports both passengers and goods. Existing methods address this using static routing methods considering neither the demands of requests nor the transfer of goods between vehicles during route planning. In this paper, we present a dynamic and demand aware fleet management framework for combined goods and passenger transportation that is capable of (1) Involving both passengers and drivers in the decision-making process by allowing drivers to negotiate to a mutually suitable price, and passengers to accept/reject, (2) Matching of goods to vehicles, and the multi-hop transfer of goods, (3) Dynamically generating optimal routes for each vehicle considering demand along their paths, based on the insertion cost which then determines the matching, (4) Dispatching idle vehicles to areas of anticipated high passenger and goods demand using Deep Reinforcement Learning (RL), (5) Allowing for distributed inference at each vehicle while collectively optimizing fleet objectives. Our proposed model is deployable independently within each vehicle as this minimizes computational costs associated with the growth of distributed systems and democratizes decision-making to each individual. Simulations on a variety of vehicle types, goods, and passenger utility functions show the effectiveness of our approach as compared to other methods that do not consider combined load transportation or dynamic multi-hop route planning.
