城市公共自行车租还不均衡的时空特征与影响因素Exploring the spatial-temporal characteristics and influence factors of supply-demand gap in bike sharing systems
于二泽,周继彪
摘要(Abstract):
准确掌握城市公共自行车租/还不均衡程度的时空规律,是引导用户参与自平衡调度、降低调度成本的重要前提。本文以宁波市中心城区公共自行车及其300 m缓冲区建成环境为研究对象,将站点的单车缺口值作为不均衡程度量化指标,基于大规模IC卡刷卡记录数据,利用Kmeans聚类与空间统计等手段,分析了全局站点单车缺口的时空变化规律及骑行OD特征,并构建考虑空间自相关的地理加权回归模型,对建成环境与单车缺口的内在联系进行实证研究。研究表明:(1)研究区域内公共自行车站点的工作日活跃特征分为2类:均衡型、就业导向型,休息日内站点特征分为3类:居住导向型、均衡型、休闲导向型;(2)工作日和休息日的站点单车缺口的全局Moran’s I分别为0.116和0.102,表现出显著的空间正相关性;(3)人口密度、主干路里程、居住社区型POI变量在早/晚高峰时段分别对单车缺口具有正向/负向影响,并且南北部地区影响效用差异明显;地铁站点的影响效用与以上变量相反,空间作用特征是随地铁2号线的走向呈梯度变化。
关键词(KeyWords): 交通工程;时空特征;地理加权回归模型;公共自行车;租还车缺口
基金项目(Foundation): 国家自然科学基金项目(52002282);; 浙江省哲学社会科学规划课题项目(21NDJC163YB)
作者(Author): 于二泽,周继彪
DOI: 10.19961/j.cnki.1672-4747.2022.04.006
参考文献(References):
- [1] EREN E, UZ V E. A review on bike-sharing:the factors affecting bike-sharing demand[J]. Sustainable Cities and Society, 2020, 54:101882.
- [2]胡正华,周继彪,周涵林,等.基于细节层次模型的公共自行车调度方法[J].交通信息与安全,2022,40(4):148-156,184.HU Zheng-hua, ZHOU Ji-biao, ZHOU Han-lin, et al. A dispatch strategy for shared bicycles based on a levels-ofdetail model[J]. Journal of Transport Information and Safety, 2022, 40(4):148-156,184.
- [3]宋苏,马佳卉,刘安迪,等.出行即服务(MAAS)实践指南介绍与案例集[R].北京:世界资源研究所, 2022.
- [4] ZHOU X. Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago[J]. PLoS One, 2015, 10(10):e0137922.
- [5] KIM M, CHO G H. Analysis on bike-share ridership for origin-destination pairs:effects of public transit route characteristics and land-use patterns[J]. Journal of Transport Geography, 2021, 93:103047.
- [6]王志军,张豫徽,季彦婕,等.基于多层线性模型的公共自行车接驳公交换乘量影响因素分析[J].交通运输工程与信息学报, 2022, 20(3):81-88.WANG Zhi-jun, ZHANG Yu-hui, JI Yan-jie, et al. Analysis of the influencing factors of public-bicycle-connecting regular bus transfer volume based on multilevel linear model[J]. Journal of Transportation Engineering and Information, 2022, 20(3):81-88.
- [7]陈红,陈恒瑞,史转转,等.公共自行车使用时空特性挖掘及租还需求预测[J].交通运输系统工程与信息,2021,21(2):238-244,250.CHEN Hong, CHEN Heng-rui, SHI Zhuan-zhuan, et al.Spatiotemporal characteristics mining and demand forecasting of shared bicycle borrow and return[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2):238-244, 250.
- [8] MA X, JI Y, YUAN Y, et al. A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data[J]. Transportation Research Part A:Policy and Practice, 2020, 139:148-173.
- [9] BAI Q, YU Z, MA S, et al. Examining influencing factors of bicycle usage for dock-based public bicycle sharing system:a case study of Xi’an, China[J]. Journal of Cleaner Production, 2022, 362:132332.
- [10]朱才华,李岩,孙晓黎,等.考虑土地利用的城市公共自行车需求预测[J].华南理工大学学报(自然科学版),2022, 50(3):9-20, 37.ZHU Cai-hua, LI Yan, SUN Xiao-li, et al. Traffic demand prediction of urban public bicycles with the consideration of land use[J]. Journal of South China University of Technology(Natural Science Edition), 2022,50(3):9-20, 37.
- [11] LIU S, ZHANG X, ZHOU C, et al. Temporal heterogeneous effects of land-use on dockless bike-sharing usage under transit-oriented development context:the case of Beijing[J]. Journal of Cleaner Production, 2022, 380:134917.
- [12] ZHANG X, CHEN Y, ZHONG Y. Spatial and temporal characteristic analysis of imbalance usage in the Hangzhou public bicycle system[J]. ISPRS International Journal of Geo-Information, 2021, 10(10):637.
- [13]高楹,宋辞,郭思慧,等.接驳地铁站的共享单车源汇时空特征及其影响因素[J].地球信息科学学报,2021,23(1):155-170.GAO Ying, SONG Ci, GUO Si-hui, et al. Spatial-temporal characteristics and influencing factors of source and sink of dockless sharing bicycles connected to subway stations[J]. Journal of Geo-Information Science, 2021,23(1):155-170.
- [14] HUA M, CHEN X, CHEN J, et al. Large-scale dockless bike sharing repositioning considering future usage and workload balance[J]. Physica A:Statistical Mechanics and Its Applications, 2022, 605:127991.
- [15] LIU X, PELECHRINIS K. Excess demand prediction for bike sharing systems[J]. PLoS One, 2021, 16(6):e0252894.
- [16] HU B, GAO Y, YAN J, et al. Understanding the operational efficiency of bicycle-sharing based on the influencing factor analyses:a case study in Nanjing, China[J]. Journal of Advanced Transportation, 2021, 2021:1-14.
- [17] XIE X F, WANG Z J. Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports:case study in the Washington D. C. area[J]. Journal of Transport Geography, 2018, 71:84-102.
- [18] HE M, MA X, WANG J, et al. Geographically weighted multinomial logit models for modelling the spatial heterogeneity in the bike-sharing renting-returning imbalance:a case study on Nanjing, China[J]. Sustainable Cities and Society, 2022, 83:103967.
- [19] KE J, YANG H, ZHENG H, et al. Hexagon-based convolutional neural network for supply-demand forecasting of ride-sourcing services[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(11):4160-4173.
- [20]李福,徐良杰,陈国俊,等.共享单车用户骑行起讫点时空特征分析[J].交通信息与安全, 2022, 40(3):146-153, 170.LI Fu, XU Liang-jie, CHEN Guo-jun, et al. An analysis of spatial-temporal characteristics of origin and destination of shared-bike users[J]. Journal of Transport Information and Safety, 2022, 40(3):146-153, 170.
- [21] ZHAO X, HU C, LIU Z, et al. Weighted dynamic time warping for grid-based travel-demand-pattern clustering:case study of Beijing bicycle-sharing system[J]. ISPRS International Journal of Geo-Information, 2019, 8(6):281.
- [22]高枫,李少英,吴志峰,等.广州市主城区共享单车骑行目的地时空特征与影响因素[J].地理研究, 2019, 38(12):2859-2872.GAO Feng, LI Shao-ying, WU Zhi-feng, et al. Spatialtemporal characteristics and the influencing factors of the ride destination of bike sharing in Guangzhou city[J]. Geographical Research, 2019, 38(12):2859-2872.
- [23]池娇,焦利民,董婷,等.基于POI数据的城市功能区定量识别及其可视化[J].测绘地理信息, 2016, 41(2):68-73.CHI Jiao, JIAO Li-min, DONG Ting, et al. Quantitative identification and visualization of urban functional area based on POI data[J]. Journal of Geomatics, 2016, 41(2):68-73.
- [24]昝雨尧,王翔,俄文娟,等.多源数据融合的城市区域时变停车需求识别方法[J].交通运输工程与信息学报, 2022, 20(2):82-94.ZAN Yu-yao, WANG Xiang, E Wen-juan, et al. Recognition and monitoring of parking in urban region based on multisource data[J]. Journal of Transportation Engineering and Information, 2022, 20(2):82-94.
- [25] JI Yan-jie, CAO Yu, LIU Yang, et al. Analysis of temporal and spatial usage patterns of dockless bike sharing system around rail transit station area[J]. Journal of Southeast University(English Edition), 2019, 35(2):228-235.
- [26] SHUAI C, SHAN J, BAI J, et al. Relationship analysis of short-term origin-destination prediction performance and spatiotemporal characteristics in urban rail transit[J]. Transportation Research Part A:Policy and Practice,2022, 164:206-223.
- [27]周航,陈学武.集时空聚类和指标筛选的公共交通通勤者识别[J].交通运输工程与信息学报, 2022, 20(1):89-97.ZHOU Hang, CHEN Xue-wu. Public transportation commuter identification based on spatio-temporal clustering and index screening[J]. Journal of Transportation Engineering and Information, 2022, 20(1):89-97.
- [28] WEI Z, ZHEN F, MO H, et al. Travel behaviours of sharing bicycles in the central urban area based on geographically weighted regression:the case of Guangzhou, China[J]. Chinese Geographical Science, 2021, 31(1):54-69.
- [29]姜晓,白璐斌,楼夏寅,等.基于多尺度时空聚类的共享单车潮汐特征挖掘与需求预测研究[J].地球信息科学学报, 2022, 24(6):1047-1060.JIANG Xiao, BAI Lu-bin, LOU Xia-yin, et al. Usage patterns identification and flow prediction of bike-sharing system based on multiscale spatiotemporal clustering[J]. Journal of Geo-information Science, 2022, 24(6):1047-1060.
- [30] DU Y, DENG F, LIAO F. A model framework for discovering the spatio-temporal usage patterns of public freefloating bike-sharing system[J]. Transportation Research Part C:Emerging Technologies, 2019, 103:39-55.