考虑乘客时空灵活性的需求响应客货联运动态调度  

Demand-responsive Dynamic Scheduling Considering Passengers'Spatio Temporal Flexibility for Passenger and Freight Transportation

作　　者：巫威眺[1] 周霄 朱彦辰 李鹏 邹弘辉 李余 WUWei-tiao;ZHOU Xiao;ZHU Yan-chen;LI Peng;ZOU Hong-hui;LI Yu(School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510641,China;School of Automobile and Transportation,Shenzhen Polytechnic,Shenzhen 518000,Guangdong,China)

机构地区：[1]华南理工大学,土木与交通学院,广州510641 [2]深圳职业技术学院,汽车与交通学院,广东深圳518000

出　　处：《交通运输系统工程与信息》2023年第4期211-227,共17页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家自然科学基金(72071079);广东省基础与应用基础研究基金(2020A1515111024);广州市重点研发计划重点专项(202103050002)。

摘　　要：以需求响应公交为代表的共享公共交通模式面临高效处理出行需求的挑战及平峰期车辆利用率低的困境。一方面,传统研究基于时空维度不可迁移的出行需求探讨运营调度优化,未从人的可移动性上探索潜在优化空间;另一方面,电子商务的发展带动了货运量的高速增长,却也面临市区货车限行和零散包裹时效性低等问题。本文聚焦共享出行服务模式新设计,提出一种考虑乘客时空灵活性的需求响应客货联运服务模式。首先,引入乘客时空灵活性的预约管理新机制,借助滚动时域框架将客货联运下的需求响应动态调度过程表述为马尔科夫决策过程,将每辆车抽象为智能体,根据动态调度特性提出适配模型的收益函数;然后,以系统总成本最少为优化目标,基于多智能体强化学习算法中的Qtran_alt框架求解;最后,以广州市黄埔区南部地区为运营环境验证模型和算法的整体性能,通过设计6个评价指标,对比Qtran_alt与另外3种多智能体强化学习算法的性能差异,并对时空灵活性、时间片间隔及客货比例进行敏感性分析。结果表明:本文模型在系统总成本增长不超过5.37%的情况下,能够额外服务50%的货物订单,其订单总服务率达到95.19%,且具备较强的普适性。Demand-responsive transit,a representative mode of a shared mobility system,faces the challenge of efficiently handling travel demand and low vehicle utilization during peak hours.Previous research investigates the optimization of operational scheduling based on the immobile travel demand in the spatiotemporal dimension and has not yet explored the potential improvements in terms of human flexibility.The development of e-commerce has resulted in the rapid growth of freight quantity,but it also faces the problems of truck restrictions in the urban area and low time efficiency of parcel delivery.This paper focuses on designing a new mode of the shared mobility system,and proposes a demand-responsive transit service mode that accommodates both passenger and freight transportation and considers passengers'spatiotemporal mobility.First,the study introduces a new reservation management mechanism for passengers'spatiotemporal flexibility,and formulates the dynamic scheduling process of demand-responsive passenger and freight combined transportation as a Markov decision process in a rolling horizon framework.Each vehicle is considered as an agent,while the information of stops,requests,and vehicles in the environment is integrated as a state of reinforcement learning.The action space is designed to meet the constraints of time,capacity and spatiotemporal mobility adjustment.The reward function of the adaptation model is developed according to the dynamic scheduling characteristics.The method minimizes the total system cost and solves the Markovian decision process model with the Qtran_alt framework based on multi-agent reinforcement learning algorithm.A test was performed in the southern area of Huangpu District,Guangzhou City,and six performance indicators were designed to validate the overall performance of the model and algorithm.The performance was compared between the Qtran_alt and three other multi-agent reinforcement learning algorithms.And the sensitivity analysis was performed for spatiotemporal mobility,time sl

关 键 词：智能交通 客货联运 多智能体强化学习 需求响应公交 动态调度 时空灵活性 

分 类 号：U491.54[交通运输工程—交通运输规划与管理]