数据驱动的共享单车停放区规划方法研究

doi:10.16097/j.cnki.1009-6744.2021.06.002

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (6): 9-16.DOI: 10.16097/j.cnki.1009-6744.2021.06.002

• 综合交通运输体系论坛 • 上一篇下一篇

数据驱动的共享单车停放区规划方法研究

郭彦茹¹，罗志雄²，王家川¹，何方^{* 2}

1. 北京市交通信息中心，北京 100076；2. 清华大学，工业工程系，北京 100084

收稿日期:2021-07-04 修回日期:2021-08-17 接受日期:2021-08-19 出版日期:2021-12-25 发布日期:2021-12-21
作者简介:郭彦茹(1987- )，女，河北冀州人，高级工程师。
基金资助:
国家重点研发计划

Data-driven Planning and Design for Bike Sharing Parking Spots

GUO Yan-ru¹ , LUO Zhi-xiong² , WANG Jia-chuan¹, HE Fang^{* 2}

1. Beijing Transportation Information Center, Beijing 100076, China; 2. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China

Received:2021-07-04 Revised:2021-08-17 Accepted:2021-08-19 Online:2021-12-25 Published:2021-12-21
Supported by:
National Key Research and Development Program of China (2018YFB1601600)

摘要/Abstract

摘要： 从宏观和微观两个角度研究共享单车停放区规划方法，宏观层面解决停放区选址定容问题，微观层面进一步优化每个停放区内部的停车位布局问题。宏观层面利用DBSCAN对共享单车停/取点进行聚类分析得到停/取需求点；然后考虑共享单车使用者路径选择、停/取点选择和停放区服务能力约束，建立混合整数线性规划模型优化停放区选址及容量。微观层面充分考虑行人、自行车与汽车之间的影响，其中行人和自行车采用改进社会力模型进行仿真，并考虑共享单车使用者最优停放区选择；汽车采用智能驾驶模型进行仿真。停车位布局通过仿真通行效率及混合交通流风险进行评价，并基于代理模型进行优化。该方法应用于北京市崇文门地铁站周边共享单车停放区规划，验证了方法的有效性，该方法可提高混合交通流通行效率，降低交通风险。

关键词: 交通工程, 停放区规划, 建模仿真, 共享单车, 混合整数规划, 仿真优化

Abstract: This paper investigates the bike-sharing parking spots planning from both macro and micro perspectives. The proposed method optimizes the location, capacity, and layout of the parking areas. At the macro level, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) generates the feasible parking locations through the bike parking/renting data cluster analysis. Then a mixed-integer linear programming model is developed to optimize the site and capacity of parking areas, considering users' path choice and parking spots choice and capacity. The micro-level analysis taking account of the interaction between pedestrians, bicycles, and vehicles. The improved social force model is used to simulate the movement of pedestrians and shared bike users. The parking area layout is evaluated through the traffic efficiency analysis. The risk level of mixed traffic simulation and is optimized by surrogate-based optimization. The proposed method has been applied to plan the bike-sharing parking areas around Chongwenmen metro station in Beijing, China. The implementation verifies the effectiveness of the method, improves the mixed traffic efficiency, and reduces the traffic risk.

Key words: traffic engineering, parking spots planning, modeling and simulation, bike sharing, mixed-integer programming, simulation-based optimization

中图分类号:

U121

郭彦茹, 罗志雄, 王家川, 何方. 数据驱动的共享单车停放区规划方法研究[J]. 交通运输系统工程与信息, 2021, 21(6): 9-16.

GUO Yan-ru , LUO Zhi-xiong , WANG Jia-chuan , HE Fang. Data-driven Planning and Design for Bike Sharing Parking Spots[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 9-16.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2021.06.002

http://www.tseit.org.cn/CN/Y2021/V21/I6/9

参考文献

[1] 李琨浩. 基于共享经济视角下城市共享单车发展对策研究 [J]. 城市, 2017(3): 66-69. [LI K H. Research on the development countermeasures of urban bike-sharing based on the perspective of sharing economy[J]. City, 2017(3): 66-69.]

[2] 李英锋.“入栏结算”是破解乱停乱放的“智能钥匙” [N]. 中国消费者报, 2019-05-30(1). [LI Y F. "Fixed point settlement" is the "intelligent key" to crack the randomly parking[N]. China Consumer Journal, 2019- 05-30(1).]

[3] GARCÍA-PALOMARES J C, GUTIÉRREZ J, LATORRE M. Optimizing the location of stations in bike-sharing programs: A GIS approach[J]. Applied Geography, 2012, 35(1-2): 235-246.

[4] ÇELEBI D, YÖRÜSÜN A, IŞIK H. Bicycle sharing system design with capacity allocations[J]. Transportation Research Part B: Methodological, 2018, 114: 86-98.

[5] LU M, AN K, HSU S C, et al. Considering user behavior in free-floating bike sharing system design: A datainformed spatial agent-based model[J]. Sustainable Cities and Society, 2019, 49: 101567.

[6] 王瑜琼, 贾顺平, 张思佳, 等. 位置识别率影响下基于博弈的共享单车停车奖惩策略研究[J]. 交通运输系统工程与信息, 2019, 19(1): 97-103. [WANG Y Q, JIA S P, ZHANG S J, et al. Reward and punishment strategy for bicycle-sharing parking based on the game theory under the influence of position recognition rate[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(1): 97-103.]

[7] HELBING D, MOLNAR P. Social force model for pedestrian dynamics[J]. Physical Review E, 1995, 51(5): 4282.

[8] MOUSSAÏD M, HELBING D, THERAULAZ G. How simple rules determine pedestrian behavior and crowd disasters[J]. Proceedings of the National Academy of Sciences, 2011, 108(17): 6884-6888.

[9] GUO N, JIANG R, WONG S C, et al. Modeling the interactions of pedestrians and cyclists in mixed flow conditions in uni-and bidirectional flows on a shared pedestrian- cycle road[J]. Transportation Research Part B: Methodological, 2020, 139: 259-284.

[10] TREIBER M, HELBING D. Realistische Mikrosimulation von Strassenverkehr mit einem einfachen Modell[C]// 16th Symposium Simulationstechnik ASIM, 2002, 2002: 80.

[11] CHAO Q, DENG Z, JIN X. Vehicle- pedestrian interaction for mixed traffic simulation[J]. Computer Animation and Virtual Worlds, 2015, 26(3/4): 405-412.

[12] ESTER M, KRIEGEL H P, SANDER J, et al. A densitybased algorithm for discovering clusters in large spatial databases with noise[C]//Kdd. 1996, 96(34): 226-231.

[13] XI H, LEE S, SON Y J. An integrated pedestrian behavior model based on extended decision field theory and social force model[M]. Human-in-the- Loop Simulations, Springer, London, 2011: 69-95.

[14] DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische Mathematik, 1959, 1(1): 269- 271.

[15] CHEN X, ZHANG L, HE X, et al. Surrogate-based optimization of expensive-to-evaluate objective for optimal highway toll charges in transportation network[J]. Computer-Aided Civil and Infrastructure Engineering, 2014, 29(5): 359-381.

[16] SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments[J]. Statistical Science, 1989: 409-423

数据驱动的共享单车停放区规划方法研究

Data-driven Planning and Design for Bike Sharing Parking Spots

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

[1]	林建新, 刘博, 赵霞, 张蕾. 基于混合约束自编码器的机动车工况智能构建方法[J]. 交通运输系统工程与信息, 2022, 22(2): 109-116.
[2]	戢晓峰, 耿昭师. 山区双车道公路货车碰撞预测的双变量冲突极值模型[J]. 交通运输系统工程与信息, 2022, 22(2): 230-238.
[3]	张建波, 孙建平, 徐春玲, 郭镜霞, 温慧敏, 宋国华. 考虑交通运行条件影响的驾驶员特征聚类[J]. 交通运输系统工程与信息, 2022, 22(2): 330-336.
[4]	马景峰, 任刚, 李豪杰, 曹奇, 杜建玮. 电动自行车与机动车事故严重性影响因素分析[J]. 交通运输系统工程与信息, 2022, 22(2): 337-348.
[5]	宗芳, 王猛, 贺正冰. 考虑多车影响的分子动力学智能网联跟驰模型[J]. 交通运输系统工程与信息, 2022, 22(1): 37-48.
[6]	马小龙, 余强, 刘建蓓. 基于路侧毫米波雷达的车辆碰撞概率计算方法[J]. 交通运输系统工程与信息, 2022, 22(1): 57-66.
[7]	廖鹏, 唐铁桥. 基于车辆动力传动和转向系统的换道轨迹优化策略[J]. 交通运输系统工程与信息, 2022, 22(1): 98-105.
[8]	刘永红, 罗霞, 胡剑鹏. 考虑临时和预约需求的共享泊位动态分配方法[J]. 交通运输系统工程与信息, 2022, 22(1): 171-181.
[9]	袁振洲, 郭曼泽, 彭泳鑫, 杨洋. 基于梯度关联规则的老年行人交通事故风险识别[J]. 交通运输系统工程与信息, 2022, 22(1): 195-208.
[10]	张玉婷, 陈波佑, 张双焱, 闫学东, 李晓梦. 抵近信控交叉口分心驾驶识别模型[J]. 交通运输系统工程与信息, 2022, 22(1): 217-224.
[11]	宋浪, 王健, 杨滨毓, 安实. 连续流交叉口左转非机动车交通组织创新设计[J]. 交通运输系统工程与信息, 2022, 22(1): 225-233.
[12]	白婧荣, 唐伯明, 孙宗元, 毕辉云, 向恬君. 毗邻互通立交特长隧道驾驶负荷研究[J]. 交通运输系统工程与信息, 2022, 22(1): 301-310.
[13]	孙启鹏, 曾开邦, 张锴琦, 杨艺琛, 张士行. 北京市共享单车出行的时空规律与需求预测研究[J]. 交通运输系统工程与信息, 2022, 22(1): 332-338.
[14]	李君羡, 沈宙彪, 童文聪, 吴志周. 基于分类车头时距的城市道路大型车影响分析[J]. 交通运输系统工程与信息, 2021, 21(6): 55-62.
[15]	卢凯, 周志洁, 张勇刚, 赵世杰, 徐广辉. 面向单向交通路网的绿波协调设计方法[J]. 交通运输系统工程与信息, 2021, 21(6): 74-83.