考虑转运与拼车的共享自动驾驶电动汽车动态运营

doi:10.16097/j.cnki.1009-6744.2025.02.006

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (2): 58-68.DOI: 10.16097/j.cnki.1009-6744.2025.02.006

• 智能交通系统与信息技术 • 上一篇下一篇

考虑转运与拼车的共享自动驾驶电动汽车动态运营

蒋阳升^a,b，叶肖甫^a,b，徐尉耀^a,b，刘洪汛^a,b，胡路^*a,b

西南交通大学，a.交通运输与物流学院；b.综合交通大数据应用技术国家工程实验室，成都610031

收稿日期:2024-10-10 修回日期:2025-01-20 接受日期:2025-02-08 出版日期:2025-04-25 发布日期:2025-04-19
作者简介:蒋阳升(1976—)，男，湖南衡阳人，教授，博士。
基金资助:
国家自然科学基金 (72371209)；四川省科学技术厅项目 (2024YFHZ0266)。

Dynamic Operation of Autonomous Shared Electric Vehicles Considering Relay and Ridesplitting

JIANG Yangsheng^a,b,YE Xiaofu^a,b,XU Weiyao^a,b,LIU Hongxun^a,b,HU Lu^*a,b

a. School of Transportation and Logistics; b. National Engineering Laboratory of Integrated Transportation Big DataApplication Technology, Southwest Jiaotong University, Chengdu 610031, China

Received:2024-10-10 Revised:2025-01-20 Accepted:2025-02-08 Online:2025-04-25 Published:2025-04-19
Supported by:
National Natural Science Foundation of China (72371209)；Department of Science and Technology of Sichuan Province (2024YFHZ0266)。

摘要/Abstract

摘要： 共享自动驾驶电动汽车是未来城市道路智能交通的重要出行方式。传统的单一快车直达模式无法充分利用车辆，导致订单满足率和经济效益较低，因此本文提出在共享自动驾驶电动汽车动态运营决策问题中开放拼车模式并引入转运策略。该问题首先以时间、空间、电池量和载客量这4个维度的信息构建四维单商品流时空网络，然后通过弧的合并简化至三维单商品流时空网络。在此基础上，建立以运营利润最大化为目标的纯整数线性规划数学模型。最后采取滚动优化方法，设置多个前视时间窗匹配快车、拼车、转运等异质类服务，以GUROBI为优化引擎对前视期子问题进行快速求解，满足实际动态运营场景。算例结果表明：车队规模与出行需求量匹配时，在较为均匀与不均匀的两种需求分布情境下，拼车模式下引入转运策略使系统运营利润较单一快车直达模式分别提高11.60%和13.85%。

关键词: 城市交通, 动态运营, 滚动时域, 共享出行

Abstract: Autonomous electric shared vehicle is an important intelligent mode for future urban transportation. The traditional single ride-hailing model fails to fully utilize vehicles, resulting in low order fulfillment rates and economic benefits. Therefore, this paper proposes a ride-splitting model and adopts relay strategies for the dynamic operational decision-making of autonomous electric shared vehicles system. The study first constructs a four-dimensional spatiotemporal network based on time, space, battery level, and passengers, which is then simplified to a three-dimensional network through arc merging. Based on this topological network, a pure integer linear programming model is developed to maximize operational profit. A rolling optimization method is introduced with multiple Forward-looking time windows matching heterogeneous services like single ride-hailing, ride-splitting, and relay service. Using GUROBI as the optimization engine, the subproblems for the Forward-looking period are solved quickly to meet practical dynamic operating scenario. A case study demonstrates that, under moderate travel demand, the introduction of ride-splitting and relay strategy increases operational profit by respectively 11.60% and 13.85% under the uniform and non uniform demand distributions.

Key words: urban traffic, dynamic operation, rolling horizon, sharing mobility

中图分类号:

U492

蒋阳升, 叶肖甫, 徐尉耀, 刘洪汛, 胡路. 考虑转运与拼车的共享自动驾驶电动汽车动态运营[J]. 交通运输系统工程与信息, 2025, 25(2): 58-68.

JIANG Yangsheng, YE Xiaofu, XU Weiyao, LIU Hongxun, HU Lu. Dynamic Operation of Autonomous Shared Electric Vehicles Considering Relay and Ridesplitting[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 58-68.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2025.02.006

http://www.tseit.org.cn/CN/Y2025/V25/I2/58

参考文献

[1]姚晓锐,王冠,杨超，等.未来城市自动驾驶共享汽车规模研究:以上海为例[J].交通运输系统工程与信息, 2019,19(6):85-91. [YAOXR,WANGG, YANGC, etal.Exploringfleet sizeof sharedautonomousvehicles in future city: Acase study inShanghai[J]. Journal of Transportation Systems Engineering and Information Technology,2019,19(6):85-91.]

[2] SHAREEFH, ISLAMMM,MOHAMEDA.Areviewof the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles[J]. Renewable andSustainableEnergyReviews, 2016, 64: 403-420.

[3] MAHMOUDIM,ZHOUX.Findingoptimal solutions for vehicle routing problem with pickup and delivery services with time windows: Adynamic programming approach based on state-space-time network representations[J]. Transportation Research Part B: Methodological,2016,89:19-42.

[4] SANTI P, RESTAG, SZELLM, et al. Quantifying the benefitsof vehiclepoolingwithshareabilitynetworks[J]. Proceedingsof theNationalAcademyofSciences,2014, 111(37):13290-13294.

[5] TUM, LIW, ORFILAO, et al. Exploring nonlinear effects of the built environment on ridesplitting: EvidencefromChengdu[J].TransportationResearchPart D:TransportandEnvironment,2021,93:102776.

[6] LU X, ZHANG Q, PENG Z, et al. Charging and relocatingoptimization for electric vehicle car-sharing: An event-based strategy improvement approach[J]. Energy,2020,207:118285.

[7] CURTALE R, LIAO F, VAN DERWAERDEN P. Understanding travel preferences for user-based relocation strategies of one-way electric car-sharing services[J]. TransportationResearch Part C: Emerging Technologies,2021,127:103135.

[8] ZHANG D, LIUY, HE S. Vehicle assignment and relays for one-way electric car-sharing systems[J]. TransportationResearchPart B:Methodological, 2019, 120:125-146.

[9]陈垚,柏赟,张安英,等.基于滚动时域优化的共享自动驾驶汽车动态调度方法[J].交通运输系统工程与信息,2022,22(3):45-52,73. [CHENY,BAIY,ZHANG A Y, et al. Dynamic fleet management of shared autonomous vehicles with rolling horizon optimization[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 45-52, 73.]

[10] LI H, HU L, JIANG Y. Dynamic pricing, vehicle relocation and staff rebalancing for station-based one way electric carsharing systems considering nonlinear charging profile[J]. Transportation Letters, 2023, 15(7): 659-684.

[11] CHEN Y, LIU Y. Integrated optimization of planning and operations for shared autonomous electric vehicle systems [J]. Transportation Science, 2023, 57(1): 106-134.

[12] IACOBUCCI R, BRUNO R. Cascaded model predictive control for shared autonomous electric vehicles systems with V2G capabilities[C]//2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, 2019: 1-7.

[13] GUO H, CHEN Y, LIU Y. Shared autonomous vehicle management considering competition with human-driven private vehicles[J]. Transportation Research Part C: Emerging Technologies, 2022, 136: 103547.

[1]	晋泽倩, 俞诚成, 叶昕. 混合效用和后悔规则下的洪涝交通疏散决策行为研究[J]. 交通运输系统工程与信息, 2025, 25(2): 16-25.
[2]	周雨阳, 赵聪颖, 李京昆, 陈艳艳, 柳堤, 王书灵. 多类城市职住及通勤状态对出行方式选择影响比较[J]. 交通运输系统工程与信息, 2025, 25(2): 26-35.
[3]	彭其渊, 刘思源, 江山, 冯涛, 陈垚, 张永祥. 贯通运营下市域铁路与地铁列车开行方案协同优化[J]. 交通运输系统工程与信息, 2025, 25(2): 36-47.
[4]	赵雪亭, 胡立伟, 周君. 城市交通拥塞风险场级联失效及韧性评估研究[J]. 交通运输系统工程与信息, 2025, 25(2): 146-156.
[5]	武慧荣, 王玉莹, 盛椿婷. 考虑动态需求的城市货运公交线路规划模型研究[J]. 交通运输系统工程与信息, 2025, 25(2): 214-226.
[6]	徐伟汉, 李欣, 王天奇. 基于生态驾驶的常规和需求响应公交混合调度方法[J]. 交通运输系统工程与信息, 2025, 25(2): 227-240.
[7]	李昊, 陈绍宽, 徐彬, 石梦彤, 肖迪, 陈梓琦. 考虑值乘能力差异的地铁跨线乘务轮班计划优化方法[J]. 交通运输系统工程与信息, 2025, 25(2): 253-260.
[8]	薛锋, 肖恩, 杨颖, 金成, 罗建. 基于多旅行商问题建模的地铁乘务排班计划优化[J]. 交通运输系统工程与信息, 2025, 25(2): 261-272.
[9]	张鹏, 吴照杰, 孙超, 李文权. 路口混合放行下的干线红波带宽最小优化模型[J]. 交通运输系统工程与信息, 2025, 25(2): 293-303.
[10]	许奇, 秦贝宁, 任澎, 陈越, 赖瑾璇. 建成环境对公交接驳地铁客流的异质性影响[J]. 交通运输系统工程与信息, 2025, 25(2): 352-363.
[11]	姚明, 王宇航, 曹淑超, 马露涵. 手机分心条件下行人运动特性研究[J]. 交通运输系统工程与信息, 2025, 25(2): 364-372.
[12]	翁剑成, 周慧缘, 张梦媛, 于江波. 考虑城市与群体异质的新能源车激励策略有效性研究[J]. 交通运输系统工程与信息, 2025, 25(1): 2-14.
[13]	李熙莹, 卢美燕, 何兆成, 苏淑妍, 庞淑敏. 基于车辆动态行为特征的交通状态识别研究[J]. 交通运输系统工程与信息, 2025, 25(1): 44-55.
[14]	王连震, 马志飞, 程国柱, 郑夫水. 城市道路智能网联汽车专用车道设置研究[J]. 交通运输系统工程与信息, 2025, 25(1): 56-66.
[15]	左忠义, 刘泽宇, 杨广川. 基于引力影响模型的轨道交通网络关键节点识别研究[J]. 交通运输系统工程与信息, 2025, 25(1): 102-112.

考虑转运与拼车的共享自动驾驶电动汽车动态运营

Dynamic Operation of Autonomous Shared Electric Vehicles Considering Relay and Ridesplitting

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics