无人驾驶条件下共享停车匹配模型及算法

doi:10.16097/j.cnki.1009-6744.2021.04.012

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (4): 99-105.DOI: 10.16097/j.cnki.1009-6744.2021.04.012

• 智能交通系统与信息技术 • 上一篇下一篇

无人驾驶条件下共享停车匹配模型及算法

何胜学^*，马思涵，程朝中，崔允汀，郁奇凡

上海理工大学，管理学院，上海 200093

收稿日期:2021-02-03 修回日期:2021-06-14 接受日期:2021-06-21 出版日期:2021-08-25 发布日期:2021-08-23
作者简介:何胜学(1976- )，男，陕西三原人，副教授，博士。
基金资助:
国家自然科学基金;上海市自然科学基金

Shared Parking Supply-demand Matching Model and Algorithm with Autonomous Vehicles

HE Sheng-xue^* , MA Si-han, CHENG Chao-zhong, CUI Yun-ting, YU Qi-fan

Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2021-02-03 Revised:2021-06-14 Accepted:2021-06-21 Online:2021-08-25 Published:2021-08-23
Supported by:
National Natural Science Foundation of China(71801153，71871144);Natural Science Foundation of Shanghai, China(18ZR1426200)。

摘要/Abstract

摘要： 针对如何减少无人驾驶车辆在泊车过程中不必要的频繁移位和因此增高的事故风险，构建了在满足合理共享停车需求条件下最小化无人车移位成本的共享停车供需匹配优化模型，并利用解的结构特征设计了一种具有针对性的模拟退火求解算法。鉴于无人车灵活变换泊位的特征，将泊车需求与泊位供给在时间上加以分割，从而使得基于分割时段的匹配模型可以反映无人车的泊车特征。将对应分割时段的匹配集合加以组合形成对应模型可行解的匹配图。利用匹配图的时空结构特征，定义对应分割时段的匹配图邻域，从而完成对模拟退火算法关键操作的设计。结果表明：利用新方法可以实现无人车的共享停车匹配；经过优化处理，无人车的移车次数可减少至不到初值的5%；一般情况下最优匹配图不唯一，为其他停车要求提供了操作空间。

关键词: 智能交通, 共享停车, 模拟退火算法, 无人驾驶车辆, 二次分配

Abstract: In order to reduce the unnecessary translocations and the incurred accident risk during the parking of autonomous vehicles, this paper formulated a shared parking supply-demand matching optimization model with satisfying the acceptable parking demand as its constraints and minimizing the cost of translocation of autonomous vehicles as its objective. Based on the structural property of the feasible solution, this paper designed a simulated annealing algorithm to solve the proposed model. Since autonomous vehicles can change the parking spaces freely, the paper divided the parking demand and the shared parking supply into small segments to make the model to reflect the feature of autonomous vehicles. Combining the matching sets with the time segments can form a matching map corresponding to a specified feasible solution of the matching model. By making use of the structural feature of matching maps, this paper defined the neighborhood of a matching map associated with a given time segment and then finished the design of the critical operations of the simulated annealing algorithm. The results show that: (a) the new method can realize the shared parking supply-demand matching with autonomous vehicles; (b) the number of translocations of autonomous vehicles can decreases to less than 5% of the initial value after optimizing; and (c) in general, the optimal matching map is not unique, which provides other parking requirements with operation possibility.

Key words: intelligent transportation, parking space sharing, simulated annealing algorithm, autonomous vehicle, quadratic assignment

中图分类号:

U491

何胜学, 马思涵, 程朝中, 崔允汀, 郁奇凡. 无人驾驶条件下共享停车匹配模型及算法[J]. 交通运输系统工程与信息, 2021, 21(4): 99-105.

HE Sheng-xue , MA Si-han, CHENG Chao-zhong, CUI Yun-ting, YU Qi-fan. Shared Parking Supply-demand Matching Model and Algorithm with Autonomous Vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 99-105.

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http://www.tseit.org.cn/CN/Y2021/V21/I4/99

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无人驾驶条件下共享停车匹配模型及算法

Shared Parking Supply-demand Matching Model and Algorithm with Autonomous Vehicles

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