交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 24-35.DOI: 10.16097/j.cnki.1009-6744.2025.01.003

• 综合交通运输体系论坛 • 上一篇    下一篇

考虑综合效益的周期型停车预约分配模型

宋现敏,刘博,李海涛*,湛天舒,李世豪,张云翔   

  1. 吉林大学,交通学院,长春130022
  • 收稿日期:2024-09-10 修回日期:2024-11-26 接受日期:2024-12-03 出版日期:2025-02-25 发布日期:2025-02-21
  • 作者简介:宋现敏(1978— ),女,山东人,教授,博士。
  • 基金资助:
    吉林省教育厅科学技术研究项目(JJKH20241298KJ);国家自然科学基金重点项目(52131202)。

APeriodic Parking Reservation and Allocation Model Considering Comprehensive Benefits

SONG Xianmin, LIU Bo, LI Haitao*, ZHAN Tianshu, LI Shihao, ZHANG Yunxiang   

  1. School of Transportation, Jilin University, Changchun 130022, China
  • Received:2024-09-10 Revised:2024-11-26 Accepted:2024-12-03 Online:2025-02-25 Published:2025-02-21
  • Supported by:
    Scientific and Technological Research Projects of the Education Department of Jilin Province (JJKH20241298KJ);Key Program of the National Natural Science Foundation of China (52131202)。

摘要: 为解决停车预约服务平台与用户之间存在的泊位运营问题,本文基于停车分配过程中服务平台的直接收益与服务水平间的关系,考虑用户出行特征的多样性,提出一种停车预约分配优化模型。为实现平台运营服务收益最大化,以运营商收益最大和用户出行成本的综合效益最小为目标建立联合优化函数,构建考虑停车分配时效性的周期型最优停车预约分配模型(POPA),并设计自适应升温的模拟退火-粒子群优化算法求解大规模停车分配问题。实验结果表明:综合考虑分配时效性和平台收益等多个因素,预约平台的最佳分配时段长度应为1h,改进算法使求解效果提高了6.14%,灵敏度分析证明了惩罚因子的引入可在不影响用户时间成本与车位利用率的情况下,使平台的用户请求接受率提升2.25%~18.17%;通过对比分析,所提模型较用户最优模型提升了38.11%的实际收益,较平台最优模型降低了15.31%的平均用户时间成本。此外,通过拓展性数值测试证明了所提模型在大规模复杂场景中的适用性和有效性。

关键词: 交通工程, 泊位运营, 整数规划模型, 停车分配, 模拟退火-粒子群优化算法

Abstract: This paper proposes a parking reservation allocation optimization method based on the relationship between the direct revenue of the service platform and its service level in the parking allocation process, as well as the diversity of users' travel characteristics. To maximize the platform's operational service revenue, an optimized function is established with the operator's maximum revenue and the minimum comprehensive benefit of user travel costs as the objective. A periodic optimal parking reservation and allocation model (POPA) is developed in consideration of the time-effectiveness of parking allocation. An adaptive heating simulated annealing-particle swarm optimization algorithm is designed to solve large-scale parking allocation problems. The experimental results show that, considering the time-effectiveness and platform revenue of multiple factors, the optimal reservation period length for the reservation platform is 1 hour. The improved algorithm improves the solution effect by 6.14%. Sensitivity analysis proves that the introduction of punishment factors can improve the platform's user request acceptance rate by 2.25% to 18.17% without affecting the user's time cost and parking lot utilization rate. The proposed model has a 38.11% higher actual revenue than the user optimal model and a 15.31% lower average user travel cost than the platform optimal model. The expanded numerical test proves the applicability and effectiveness of the proposed model in large-scale complex scenarios.

Key words: traffic engineering, terminal operations, integer programming, parking allocation, simulated annealing and particle swarm optimization algorithm

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