空间异质性下共享单车出行量的非线性影响

doi:10.3785/j.issn.1008-973X.2025.12.012

浙江大学学报(工学版)

2025, Vol. 59

Issue (12): 2576-2584 DOI: 10.3785/j.issn.1008-973X.2025.12.012

交通工程、土木工程

空间异质性下共享单车出行量的非线性影响

路庆昌(

),袁康洁

长安大学电子与控制工程学院，陕西西安 710064

Nonlinear effects of bike-sharing demands considering spatial heterogeneity

Qingchang LU(

),Kangjie YUAN

School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China

全文: PDF(1820 KB) HTML

摘要：

为了探究空间异质性对建成环境与共享单车出行量之间非线性关系的影响，构建考虑空间异质性的GW-XGBoost模型，采用SHAP模型解释建成环境因素的作用程度和空间差异. 相较于地理加权回归和极端梯度提升树模型，模型GW-XGBoost通过引入地理空间加权和自适应带宽显著提升了模型解释力和预测力，整体拟合优度平均提高15.59%，同时能够揭示建成环境对单车出行量非线性影响的强度方向和局部差异. 结果显示，建成环境因素对单车出行量呈现非线性影响. 在人口密度增加到20 000 人/km²后，影响由负转正；离CBD的距离位于15~20 km时，由中心向外围影响效应由正转负随后趋于平稳；在容积率增加到1.8后，影响效应由负转正. 研究结果为城市共享单车系统的资源优化提供科学依据和方法支撑.

关键词： 共享单车; 建成环境; 机器学习; 空间异质性; 非线性影响

Abstract:

A GW-XGBoost model considering spatial heterogeneity was constructed, and the SHAP model was used to explain the extent and spatial differences in the role of built environment factors, in order to explore the influence of spatial heterogeneity on the nonlinear relationship between the built environment and bike-sharing trips. Compared with the geographically weighted regression and extreme gradient boosting tree models, the GW-XGBoost model significantly improved the explanatory and predictive power of the model by introducing geospatial weighting and adaptive bandwidth, with the overall goodness-of-fit increased by 15.59% on average, and it could reveal the intensity, direction and local differences of the built environment on the nonlinear impact of bike-sharing trips. The results showed that the built environment factors had a nonlinear impact on bike-sharing trips. When the population density reached 20000 persons per km², the impact turned from negative to positive. When the distance from CBD factor was between 15 and 20 km, its effect shifted from positive to negative, and then became stabilized when moving outward from the city center. When the floor area ratio reached 1.8, the impact effect turned from negative to positive. The research results provide a scientific basis and methodological support for the resource optimization of the urban bike-sharing system.

Key words: bike-sharing built environment machine learning spatial heterogeneity nonlinear effect

收稿日期: 2024-12-27 出版日期: 2025-11-25

CLC:

U 491.1

基金资助: 国家自然科学基金资助项目（72471035）.

作者简介: 路庆昌（1984—），男，教授，博士，从事智能交通系统分析、交通行为与环境研究. orcid.org/0000-0001-9616-2271. E-mail：qclu@chd.edu.cn

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引用本文:

路庆昌,袁康洁. 空间异质性下共享单车出行量的非线性影响[J]. 浙江大学学报(工学版), 2025, 59(12): 2576-2584.

Qingchang LU,Kangjie YUAN. Nonlinear effects of bike-sharing demands considering spatial heterogeneity. Journal of ZheJiang University (Engineering Science), 2025, 59(12): 2576-2584.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.12.012 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I12/2576

图 1 研究区域及共享单车出行量分布图

表 1 变量描述性统计及检验结果

表 2 模型性能比较

图 2 GW-XGBoost模型R2的分布

图 3 影响变量的相对重要性

表 3 GW-XGBoost模型的局部SHAP

图 4 人口密度SHAP分布

图 5 到CBD的距离SHAP分布

图 6 容积率SHAP分布

图 7 人口密度对共享单车出行量的影响

图 8 到CBD的距离对共享单车出行量的影响

图 9 容积率对共享单车出行量的影响

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 doi: 10.1016/j.scs.2019.101882
2	CHEN Z, VAN LIEROP D, ETTEMA D Dockless bike-sharing systems: what are the implications?[J]. Transport Reviews, 2020, 40 (3): 333- 353 doi: 10.1080/01441647.2019.1710306
3	WANG X, LINDSEY G, SCHONER J E, et al Modeling bike share station activity: effects of nearby businesses and jobs on trips to and from stations[J]. Journal of Urban Planning and Development, 2016, 142: 04015001 doi: 10.1061/(ASCE)UP.1943-5444.0000273
4	ALCORN L G, JIAO J Bike-sharing station usage and the surrounding built environments in major Texas Cities[J]. Journal of Planning Education and Research, 2023, 43 (1): 122- 135 doi: 10.1177/0739456X19862854
5	WU C, KIM I, CHUNG H The effects of built environment spatial variation on bike-sharing usage: a case study of Suzhou, China[J]. Cities, 2021, 110: 103063 doi: 10.1016/j.cities.2020.103063
6	SUN Y, WANG Y, WU H How does the urban built environment affect dockless bikesharing-metro integration cycling? Analysis from a nonlinear comprehensive perspective[J]. Journal of Cleaner Production, 2024, 449: 141770 doi: 10.1016/j.jclepro.2024.141770
7	徐标, 路庆昌共享单车停车需求的多尺度时空影响因素[J]. 浙江大学学报: 工学版, 2023, 57 (2): 380- 391 XU Biao, LU Qingchang Multi-scale spatiotemporal influencing factors of bike-sharing parking demand[J]. Journal of Zhejiang University: Engineering Science, 2023, 57 (2): 380- 391
8	YANG L, YU B, LIANG Y, et al Time-varying and non-linear associations between metro ridership and the built environment[J]. Tunnelling and Underground Space Technology, 2023, 132: 104931 doi: 10.1016/j.tust.2022.104931
9	DING C, CAO X, WANG Y Synergistic effects of the built environment and commuting programs on commute mode choice[J]. Transportation Research Part A: Policy and Practice, 2018, 118: 104- 118 doi: 10.1016/j.tra.2018.08.041
10	HATAMI F, RAHMAN M M, NIKPARVAR B, et al Non-linear associations between the urban built environment and commuting modal split: a random forest approach and SHAP evaluation[J]. IEEE Access, 2023, 11: 12649- 12662 doi: 10.1109/ACCESS.2023.3241627
11	WANG S, GAO K, ZHANG L, et al Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US[J]. Accident Analysis and Prevention, 2024, 199: 107528 doi: 10.1016/j.aap.2024.107528
12	LV H, LI H, CHEN Y, et al An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning[J]. Journal of Transport Geography, 2023, 113: 103716 doi: 10.1016/j.jtrangeo.2023.103716
13	WANG Y, ZHAN Z, MI Y, et al Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity[J]. Transportation Research Part D: Transport and Environment, 2022, 104: 103194 doi: 10.1016/j.trd.2022.103194
14	ZHOU T, FENG T, KEMPERMAN A Assessing the effects of the built environment and microclimate on cycling volume[J]. Transportation Research Part D: Transport and Environment, 2023, 124: 103936 doi: 10.1016/j.trd.2023.103936
15	ZHU B, HU S, KAPARIAS I, et al Revealing the driving factors and mobility patterns of bike-sharing commuting demands for integrated public transport systems[J]. Sustainable Cities and Society, 2024, 104: 105323 doi: 10.1016/j.scs.2024.105323
16	深圳市人大常委会. 深圳经济特区互联网租赁自行车管理若干规定[EB/OL]. (2021−07−06) [2024−12−20]. https://www.szrd.gov.cn/v2/zx/szfg/content/post_966176.html.
17	WU J, TA N, SONG Y, et al Urban form breeds neighborhood vibrancy: a case study using a GPS-based activity survey in suburban Beijing[J]. Cities, 2018, 74: 100- 108 doi: 10.1016/j.cities.2017.11.008
18	仝德, 高静, 龚咏喜城中村对深圳市职住空间融合的影响: 基于手机信令数据的研究[J]. 北京大学学报: 自然科学版, 2020, 56 (6): 1091- 1101 TONG De, GAO Jing, GONG Yongxi Impact of urban village on job-housing balance in Shenzhen: a study using mobile phone signaling data[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56 (6): 1091- 1101
19	LI M, TU W, TONG H, et al Quantifying the nighttime economy–housing separation from a human activity standpoint: a case study in Shenzhen, China[J]. Cities, 2024, 148: 104894 doi: 10.1016/j.cities.2024.104894
20	朱岑远, 郑乐, 张毅萌基于MGWR的共享单车空间异质性分析[J]. 物流科技, 2024, 47 (24): 72- 77 ZHU Cenyuan, ZHENG Yue, ZHANG Yimeng Spatial heterogeneity analysis of bikesharing based on MGWR[J]. Logistics Sci-Tech, 2024, 47 (24): 72- 77
21	YAO Y, LIU X, LI X, et al Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata[J]. International Journal of Geographical Information Science, 2017, 31 (12): 2452- 2479 doi: 10.1080/13658816.2017.1360494
22	GUO Y, YANG L, CHEN Y Bike share usage and the built environment: a review[J]. Frontiers in Public Health, 2022, 10: 848169 doi: 10.3389/fpubh.2022.848169
23	孙艺玲, 仝德, 曹超城市建成环境对公共自行车使用的影响机制研究: 以深圳市南山区为例[J]. 北京大学学报: 自然科学版, 2018, 54 (6): 1325- 1331 SUN Yiling, TONG De, CAO Chao How urban built environment affects the use of public bicycles: a case study of Nanshan District of Shenzhen[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2018, 54 (6): 1325- 1331
24	关昊天, 戢晓峰, 李武, 等建成环境对共享单车与地铁组合出行的影响关系[J]. 交通运输系统工程与信息, 2024, 24 (4): 200- 211 GUAN Haotian, JI Xiaofeng, LI Wu, et al Influence of built environment on integrated use of bike sharing and metro[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24 (4): 200- 211
25	EWING R, CERVERO R Travel and the built environment: a synthesis[J]. Transportation Research Record: Journal of the Transportation Research Board, 2001, 1780 (1): 87- 114 doi: 10.3141/1780-10
26	CHEN T, GUESTRIN C. XGBoost: a scalable tree boosting system [C]// 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 785−794.
27	XIAO L, LO S, LIU J, et al Nonlinear and synergistic effects of TOD on urban vibrancy: applying local explanations for gradient boosting decision tree[J]. Sustainable Cities and Society, 2021, 72: 103063 doi: 10.1016/j.scs.2021.103063
28	YANG W, LI Y, LIU Y, et al Environmental factors for outdoor jogging in Beijing: insights from using explainable spatial machine learning and massive trajectory data[J]. Landscape and Urban Planning, 2024, 243: 104969 doi: 10.1016/j.landurbplan.2023.104969
29	成骋, 陈文栋, 马洪生, 等基于Leiden算法的共享单车活动社区识别方法: 南京案例分析[J]. 交通信息与安全, 2023, 41 (2): 103- 111,156 CHENG Cheng, CHEN Wendong, MA Hongsheng, et al A method for identifying operation zones of free-floating shared bikes based on leiden algorithm: a case study of the city of Nanjing[J]. Journal of Transport Information and Safety, 2023, 41 (2): 103- 111,156
30	WANG J, WANG Z, WANG Z, et al Exploring the effect of neighbouring built and demographic environment on station-level bike-sharing trips under COVID-19[J]. Journal of Transport and Health, 2024, 36: 101818
31	BI H, LI A, HUA M, et al Examining the varying influences of built environment on bike-sharing commuting: empirical evidence from Shanghai[J]. Transport Policy, 2022, 129: 51- 65 doi: 10.1016/j.tranpol.2022.10.004
32	严亚磊, 于涛, 沈丽珍共享单车出行的建成环境影响机制: 以上海市为例[J]. 上海城市规划, 2020, (6): 85- 91 YAN Yalei, YU Tao, SHEN Lizhen The impact mechanism of built environment on shared bikes travel: a case study of Shanghai[J]. Shanghai Urban Planning Review, 2020, (6): 85- 91

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