建成环境对共享单车使用特征的非线性影响研究

doi:10.16097/j.cnki.1009-6744.2024.02.022

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (2): 217-224.DOI: 10.16097/j.cnki.1009-6744.2024.02.022

建成环境对共享单车使用特征的非线性影响研究

陈奕璠¹，张步镐²，党振¹，郭唐仪¹，顾子渊³，张玉梁^*4

1. 南京理工大学，自动化学院，南京210094；2.银江技术股份有限公司，浙江省智能交通工程技术研究中心，杭州310040；3. 东南大学，交通学院，南京211189；4.浙江大学，智能交通研究所，杭州310058

收稿日期:2023-12-28 修回日期:2024-02-20 接受日期:2024-02-26 出版日期:2024-04-25 发布日期:2024-04-25
作者简介:陈奕璠(1993- )，女，江苏南京人，讲师。
基金资助:
国家重点研发计划(2019YFE0123800)；浙江省智能交通工程技术研究中心开放课题 (2023ERCITZJ-KF11)；江苏省重大科技示范项目 (BE2022860)。

Exploring Nonlinear Effects of Built Environment on Dockless Bike Sharing Usage

CHENYifan¹,ZHANG Buhao²,DANG Zhen¹,GUOTangyi¹,GU Ziyuan³,ZHANGYuliang^*4

1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China; 2. Zhejiang Intelligent Transportation Engineering Technology Research Center, Enjoyer Technology Co Ltd, Hangzhou 310040, China; 3. School of Transportation, Southeast University, Nanjing 211189, China; 4. Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China

Received:2023-12-28 Revised:2024-02-20 Accepted:2024-02-26 Online:2024-04-25 Published:2024-04-25
Supported by:
NationalKeyResearchandDevelopmentProgram of China (2019YFE0123800)；Zhejiang Intelligent Transportation Engineering Technology Research Center Open Project (2023ERCITZJ-KF11)；MajorScienceandTechnology Demonstration Projects in Jiangsu Province (BE2022860)。

摘要/Abstract

摘要： 为研究共享单车使用特征与建成环境的依赖关系，本文以2020年厦门市共享单车订单数据和电子围栏信息为基础，从集计(栅格区域)和非集计(个体出行)两个层面，利用极端梯度提升模型(XGboost)，探究建成环境对共享单车使用特征的非线性解释能力。首先，识别密度、设计、目的地可达性、土地利用多样性、公共交通可达性和需求管理六个维度的建成环境变量对单车出行生成、吸引以及用户出发时间选择的相对重要性。之后，根据部分依赖图，揭示建成环境变量对单车使用特征指标的影响趋势。结果表明，在集计层面，电子围栏密度是最重要的建成环境因素，对出行生成和吸引的影响程度分别为26.88%和51.90%，且在150个·km-2附近产生阈值效应。在非集计层面，单车用户早高峰借车概率与出行起讫点的建成环境均有关联。其中，目的地栅格中工作场所比例影响最显著(18.17%)，出发地栅格CBD邻近度(7.34%)和出发地栅格公交站点密度(5.91%)次之。

关键词: 城市交通, 出行特征, 极端梯度提升模型, 共享出行, 建成环境, 非线性分析

Abstract: To investigate the dependency of dockless bike usage characteristics on the built environment, this paper used dockless bike order data and electronic fence information from Xiamen city in 2020 to analyze the nonlinear explanatory power of the built environment at both the aggregated (grid-level) and disaggregated (individual trips) levels. A machine learning model, namely, extreme gradient boosting model (XGBoost) was adopted. First, the relative importance of six dimensions of built environment variables (density, design, destination accessibility, land use diversity, public transport accessibility, and demand management) were identified on bike trip generation, attraction, and the user's departure time choice. Then, according to partial dependence plots, the impact trends and the threshold effects of built environment variables were evaluated. The results revealed that at the aggregate level, electronic fence density was the most critical factor, affecting travel generation and attraction by 26.88% and 51.88% respectively. A threshold effect was approximately 150 per · km-2. At the disaggregate level, the probability of dockless bike users borrowing bikes during the morning peak was associated with the built environment features of both the origins and destinations. Among these, the proportion of workplaces in the destination grid was the most significant factor (18.17%), followed by the proximity to Central Business District (CBD) of the origin grid (7.34%) and bus stop density in the origin grid (5.91%).

Key words: urban traffic, travel characteristics, XGboost, shared mobility, built environment, nonlinear analysis

中图分类号:

U491

陈奕璠, 张步镐, 党振, 郭唐仪, 顾子渊, 张玉梁. 建成环境对共享单车使用特征的非线性影响研究[J]. 交通运输系统工程与信息, 2024, 24(2): 217-224.

CHENYifan, ZHANG Buhao, DANG Zhen, GUOTangyi, GU Ziyuan, ZHANGYuliang. Exploring Nonlinear Effects of Built Environment on Dockless Bike Sharing Usage[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(2): 217-224.

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http://www.tseit.org.cn/CN/Y2024/V24/I2/217

参考文献

[1]CHEN Y F, GU Z Y, ZHENG N, et al. Optimal coordinated congestion pricing for multiple regions: A surrogate-based approach[J/OL]. Transportation, (2023 06-20) [2023-12-01]， https://link.springer.com/article/ 10.1007/s11116-023-10400-5#citeas.

[2] WANG X Q, YIN C Y, ZHANG J Y, et al. Nonlinear effects of residential and workplace built environment on car dependence[J]. Journal of Transport Geography, 2021, 96: 103207.

[3] CHEN Z Y, LIEROP D V, ETTEMA D. Dockless bike sharing systems: What are the implications?[J]. Transport Reviews, 2020, 40(3): 333-353.

[4] MOONEY S J, HOSFORD K, HOWE B, et al. Freedom from the station: Spatial equity in access to dockless bike share[J]. Journal of Transport Geography, 2019, 74: 91-96.

[5] 中国道路运输协会.互联网租赁自行车行业数据信息统计和分析报告[R].北京:中国道路运输协会,2022. [China Road Transport Associations. Statistical and analytical report on the dockless bike industry[R]. Beijing: CRTA, 2022.]

[6]CHEN Z Y, LIEROP D V, ETTEMA D. Travel satisfaction with dockless bike-sharing: Trip stages, attitudes and the built environment[J]. Transportation Research Part D: Transport and Environment, 2022, 106: 103280.

[7]XIAO D, KIM I, ZHENG N. Recent advances in understanding the impact of built environment on traffic performance[J]. Multimodal Transportation, 2022, 1(4): 100034.

[8] CERVERO R, KOCKELMAN K. Travel demand and the 3Ds: Density, diversity, and design[J]. Transportation Research Part D: Transport and Environment, 1997, 2(3): 199-219.

[9] MATEO-BABIANO I, BEAN R, CORCORAN J, et al. How does our natural and built environment affect the use of bicycle sharing?[J]. Transportation Research Part A: Policy and Practice, 2016, 94: 295-307.

[10] ZHAO P, LI S. Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing[J]. Transportation Research Part A: Policy and Practice, 2017, 99: 46-60.

[11] DURAN-RODAS D, CHANIOTAKIS E, ANTONIOU C. Built environment factors affecting bike sharing ridership: Data-driven approach for multiple cities[J]. Transportation Research Record, 2019, 2673: 55-68.

[12] 孙超，陆建.共享单车出行空间异质性特征及驱动因素研究[J]. 交通运输系统工程与信息,2022,22 (3): 198-206. [SUN C, LU J. Bike-sharing trips spatial heterogeneity and driving factors[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 198-206.]

[13] GÄRLING T. A conceptual analysis of the impact of travel demand management on private car use[J]. Transport Policy, 2002, 9(1): 59-70.

[14] EWING R, CERVERO R. Travel and the built environment: A synthesis[J]. Transportation Research Record, 2001, 1780: 87-114.

[15] CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system[C]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). San Francisco: Association for Computing Machinery, 2016.

建成环境对共享单车使用特征的非线性影响研究

Exploring Nonlinear Effects of Built Environment on Dockless Bike Sharing Usage

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