考虑安全与效率的路外停车场内部车行交通组织优化方法

吴东平; 聂小虎; 常红光; 朱顺应

doi:10.3963/j.jssn.1674-4861.2023.05.014

考虑安全与效率的路外停车场内部车行交通组织优化方法

doi: 10.3963/j.jssn.1674-4861.2023.05.014

1.
中交第二公路勘察设计研究院有限公司武汉 430052
2.
武汉理工大学交通与物流工程学院武汉 430063

基金项目:

国家自然科学基金项目 52272337

武汉经开区2022年度“车谷英才计划”行业高端人才项目 20223225-4

详细信息

通讯作者:
吴东平（1980—），硕士，正高级工程师. 研究方向：交通规划设计. E-mail：124440189@qq.com

中图分类号: U491.7
计量
- 文章访问数: 320
- HTML全文浏览量: 194
- PDF下载量: 15
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-16
- 网络出版日期: 2024-01-18

An Optimization Method for Internal Vehicle-traffic Organization in Off-street Parking Lot Considering Safety and Efficiency

1.
CCCC Second Highway Consultants Co., Ltd., Wuhan 430052, China
2.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

摘要

摘要: 为提高路外停车场停车效率，同时保障停车过程的交通安全，研究了路外停车场内部车行交通组织优化方法。利用有向加权图表征停车场出入口和车行通道布局及内部车行交通组织，将车行交通组织优化问题转化为邻接矩阵优化问题；以停车过程中的安全与效率为优化目标，构建潜在冲突风险、泊车行程时间和节点均衡系数3个评价指标，考虑停车场内部泊位数量和通道通行能力的约束，建立停车场内部车行交通组织优化模型，并采用遗传算法求解。为比较优化前后交通组织效果，基于实际案例数据进行VISSIM仿真，选取出入口排队长度、单车泊车时间、冲突点分布及车位利用率进行对比研究，并进行模型参数和交通流量的敏感性分析。结果表明：①模型能够弥补定性研究的不足和主观经验判断的缺陷，实现路外停车场内部车行交通组织定量优化。②优化后出入口排队长度平均降低了25.8%，车位利用率在[0, 1.8]范围内的停车单元数下降了5.89%；冲突点核密度降低。③模型结果对潜在冲突风险参数在±0.1~±0.3范围内的变化不敏感，模型较为稳定；在-20%~+20%的流量变化范围内，优化方案单车泊车时间及平均排队长度变化范围均维持在10%以内，能够适应实际应用场景下的流量波动。
- 交通组织 /
- 路外停车场 /
- 优化模型 /
- 遗传算法 /
- 安全与效率
Abstract: To improve the efficiency of off-street parking lots and to ensure road traffic, this study proposes an optimization method for internal vehicle-traffic organization in off-street parking lots. A directed weighted graph is utilized to represent the layout of exit/entrance and passages of a parking lot and the traffic organization within the parking lot, thereby transforming the optimization problem of traffic organization into the optimization problem of adjacency matrix. With safety and efficiency as the optimization objectives, three evaluation indicators, i.e., potential conflict risk, parking travel time and node equilibrium coefficient are used. Thereby the optimization model for internal traffic organization of parking lots is established, considering the constraints of the number of parking spots and passage capacity. The optimization problem is solved using genetic algorithm. To compare the effects of traffic organization before and after optimization, VISSIM-simulation is adopted based on data from an empirical case. The parameters such as queue length at entrances/exits, individual parking time, distribution of conflict points, and use ratio of parking spots are selected for comparison, along with sensitivity analysis of model parameters and traffic flow. The results show that: ① the model can compensate for the limitations of qualitative research and subjective empirical judgements, achieving quantitative optimization of the internal traffic organization in off-street parking lots. ② The queue lengths at entrances/exits reduces by 25.8% on average; the number of parking spots with use ratio in the range of 0 to 1.8 decreases by 5.89%; and the kernel density of conflict points also reduces. ③ The model is relatively stable as it is insensitive to the variations of parameters of potential conflict risks within the range of ± 0.1 to ±0.3. 4) Within a range of -20% to +20% regarding the variation of traffic volume, the optimized solution ensures the corresponding variations of individual parking time and the average queue length remain within 10%, which shows that the solution is adaptable to fluctuating traffic volumes in real-world scenarios.
- traffic organization /
- off-street parking lot /
- optimization model /
- genetic algorithm /
- safety and efficiency

HTML全文

图 1 泊车车辆分配流程

Figure 1. Parking vehicle allocation process

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图 2 遗传算法求解流程

Figure 2. Genetic algorithm solution process

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图 3 案例停车场平面布局

Figure 3. Layout of parking lot

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图 4 遗传算法优化过程

Figure 4. Optimization process for genetic algor

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图 5 优化后内部车行交通组织

Figure 5. Vehicle traffic organization after optimization

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图 6 出入口排队长度对比

Figure 6. Comparison of queue length at entrance and exit

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图 7 单车泊车时间对比

Figure 7. Comparison of parking time per vehicle

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图 8 冲突点分布对比

Figure 8. Comparison of conflict point distribution

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图 9 车位占用率时间分布

Figure 9. Time distribution of parking space occupancy

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图 10 车位利用率对比

Figure 10. Comparison of parking space utilization

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图 11 潜在冲突风险参数的敏感性分析

Figure 11. Sensitivity analysis for parameters of potential conflict risk

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图 12 优化方案的流量敏感性分析

Figure 12. Traffic sensitivity analysis after optimization

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表 1 分出入口驶入驶出车辆

Table 1. Entering and exiting vehicles of different entrance

时段	1号驶入	1号驶出	2号驶入	2号驶出
17：00—17：15	32	21	44	16
17：15—17：30	40	17	32	22
17：30—17：45	46	19	29	27
17：45—18：00	25	16	37	26
18：00—18：15	27	22	26	20
18：15—18：30	36	25	34	28
18：30—18：45	28	21	39	17
18：45—19：00	38	13	45	26

下载: 导出CSV

参考文献(22)

[1]	杨碧云. 大型停车场分区及流线优化设计[D]. 兰州: 兰州交通大学, 2020. YANG B Y. Optimal design of zoning and circulation of large parking lot[D]. Lanzhou: Lanzhou Jiaotong University, 2020. (in Chinese)
[2]	陈安迪. 结合老城区地形高差的楼宇地下空间研究—以南京大学硅巷文创中心项目设计为例[D]. 南京: 南京大学, 2020. CHEN A D. Study on the underground space of buildings combined with the terrain elevation difference in the old city area-a case study of the wenchuang center in Nanjing University[D]. Nanjing: Nanjing University, 2020. (in Chinese)
[3]	YOUNG W, THOMPSON R. A computer graphics approach for the determination of parking lot layouts[J]. Traffic Engineering and Control, 1987, 28(3): 120-123.
[4]	PJHJ W, BORGERS A, TIMMERMANS H. Travelers micro-behavior at parking lots - a model of parking choice behavior[C]. 82nd Annual Meeting of the Transportation Research Board, Washington DC, United States: TRID, 2003.
[5]	孙博, 焦朋朋, 张羽佳. 考虑个体停车需求的医院泊位分配优化[J]. 交通信息与安全, 2020, 38(1): 127-135. doi: 10.3963/j.jssn.1674-4861.2020.01.016 SUN B, JIAO P P, ZHANG Y J. Optimization of hospital parking allocation considering individual parking demands[J]. Journal of Transport Information and Safety, 2020, 38(1): 127-135. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.01.016
[6]	WU M K, JIANG H B, TAN C A. Automated parking space allocation during transition with both human-operated and autonomous vehicles[J]. Applied Sciences, 2021, 11(2): 855. doi: 10.3390/app11020855
[7]	佘飞. 大型停车场空闲车位短时预测及其路径诱导研究[D]. 兰州: 兰州交通大学, 2018. SHE F. Research on short term prediction of unoccupied parking space number and path guidance in large parking lots[D]. Lanzhou: Lanzhou Jiaotong University, 2018. (in Chinese)
[8]	孙剑萍, 徐昀, 李晓鹏, 等. 城市停车场路径规划系统设计与实现[J]. 现代电子技术, 2021, 44(21): 83-89. SUN J P, XU Y, LI X P, et al. Design and implementation of urban parking path planning system[J]. Modern Electronics Technique, 2021, 44(21): 83-89. (in Chinese).
[9]	王维, 盖之华. 基于Dijkstra算法的停车场泊车引导路径设计[J]. 网络安全技术与应用, 2018(9): 52-53. doi: 10.3969/j.issn.1009-6833.2018.09.029 WANG W, GAI Z H. Parking guidance path design of parking lot based on dijkstra algorithm [J]. Network Security Tech-nology & Application, 2018(9): 52-53. (in Chinese) doi: 10.3969/j.issn.1009-6833.2018.09.029
[10]	周必扬, 常玉林, 孙超. 基于大型停车场停车冲突的路径优化研究[J]. 重庆理工大学学报(自然科学), 2021, 35(2): 26-33, 59. ZHOU B Y, CHANG Y L, SUN C. Research on path optimization based on parking conflict in large parking lot[J]. Journal of Chongqing University of Technology(Natural Science), 2021, 35(2): 26-33, 59. (in Chinese).
[11]	张家旭, 王志伟, 郭崇, 等. 面向动态障碍物场景的自主代客泊车路径规划[J]. 东南大学学报(自然科学版), 2022, 52(2): 369-376. ZHANG J X, WANG Z W, GUO C, et al. Autonomous valet parking path planning for dynamic obstacle scene[J]. Journal of Southeast University(Natural Science Edition), 2022, 52(2): 369-376. (in Chinese)
[12]	ZUO Y, WU Y, MIN G, et al. Learning-based network path planning for traffic engineering[J]. Future Generation Computer Systems, 2019, 92: 59-67. doi: 10.1016/j.future.2018.09.043
[13]	谢劲, 胡光元, 闫明, 等. 基于粒子群与人工鱼群混合的泊车路径算法[J]. 控制工程, 2022, 29(12): 2357-2364. XIE J, HU G Y, YAN M, et al. A hybrid algorithm for parking path planning based on particle swarm optimization algorithm and artificial fish swarm algorithm[J] Control Engineering of China, 2022, 29(12): 2357-2364. (in Chinese)
[14]	晏勇, 雷航, 梁潘. 基于改进型蚁群算法自适应停车引导系统的设计[J]. 实验技术与管理, 2020, 37(3): 80-82. YAN Y, LEI H, LIANG P. Design of adaptive parking guidance system based on improved ant colony algorithm[J]. Experimental Technology and Management, 2020, 37(3): 80-82. (in Chin-ese
[15]	范益群, 张竹, 杨彩霞. 城市地下交通设施规划与设计[M]. 上海: 同济大学出版社, 2015. FAN Y Q, ZHANG Z, YANG C X. Planning and design of urban underground transportation facilities[M]. Shanghai: Tongji University of Press, 2015. (in Chinese)
[16]	XIE Z, WU X, GUO J, et al. Parking lot allocation model considering conversion between dynamic and static traffic[J]. Journal of Intelligent & Fuzzy Systems, 2021, 41(4): 5207-5217.
[17]	THULASIRAMAN K, NISHIZEKI T, XUE G. The handbook of graph algorithms and applications, Volume 1: Theory and optimization[M]. United Kingdom: Chapman & Hall/CRC, 2010.
[18]	郭凤香, 熊昌安. 城市道路平面交叉口复杂度研究进展[J]. 科学技术与工程, 2021, 21(6): 2141-2150. GUO F X, XIONG C A. A review of progress in studying the complexity degree of urban road intersection[J]. Science Technology and Engineering, 2021, 21(6): 2141-2150. (in Chinese)
[19]	田志立, 周海涛. 容量限制分配方法的应用[J]. 公路交通科技, 1995, 12(1): 49-53. TIAN Z L, ZHOU H T. Application of capacity restrained traffic assignment method[J]. Journal of Highway and Transportation Research and Development, 1995, 12(1): 49-53. (in Chinese)
[20]	GAL M S, RUBINFELD D L. Data standard-ization[J]. New York University Law Review, 2019(4): 737-770.
[21]	孙龙武. 基于GIS的停车场规划体系研究及应用[D]. 武汉: 武汉工程大学, 2017. SUN L W. Research on the planning for parking lot based on GIS[D]. Wuhan: Wuhan Institude of Technology, 2017. (in Chinese)
[22]	KUSUM D, KRISHNA P S, KANSAL M L. A real coded genetic algorithm for solving integer and mixed integer optimization problems[J]. Applied Mathematics and Computation, 2009(2): 505-518.