[1] BIAN Yang, YIN Luyao, ZHAO Xiaohua, HAN Tangshan, et al. Research on the discrimination of detour behavior and influencing factors of shared bicycles [J]. Journal of Southeast University (English Edition), 2025, 41 (2): 215-225. [doi:10.3969/j.issn.1003-7985.2025.02.011]

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Research on the discrimination of detour behavior and influencing factors of shared bicycles()

共享单车绕行行为判别及影响因素研究

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

41

Issue:

2025 2

Page:

215-225

Research Field:

Traffic and Transportation Engineering

Publishing date:

2025-06-17

Info

Title:

Research on the discrimination of detour behavior and influencing factors of shared bicycles

共享单车绕行行为判别及影响因素研究

Author(s):

BIAN Yang¹, YIN Luyao¹, ZHAO Xiaohua¹, HAN Tangshan², ZHANG Xiaolong¹

1.Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
2.China Highway Engineering Consulting Corporation Limited, Beijing 100089, China

边扬¹, 尹璐瑶¹, 赵晓华¹, 韩唐姗², 张晓龙¹

1.北京工业大学城市建设学部，北京 100124
2.中国公路工程咨询集团有限公司，北京 100089

Keywords:

shared bicycles;  detour behavior;  trajectory data;  machine learning;  influencing factor

共享单车; 绕行行为; 轨迹数据; 机器学习; 影响因素

PACS:

U491.1

DOI:

10.3969/j.issn.1003-7985.2025.02.011

Abstract:

To investigate the distribution characteristics and influencing factors of bicycle detour behavior, this study accurately identified detour behavior using global positioning system (GPS) track data from shared bicycles. Factors such as travel time, road conditions, public transportation facilities, and land use types were considered in constructing a detour behavior influence model based on the CatBoost machine learning algorithm. The interpretability of the machine learning framework was enhanced via Shapley additive explanations (SHAP), enabling an analysis of the impact of each factor on detour behavior. The results indicated that the CatBoost model effectively recognized detour behavior with high accuracy. The frequency of detour behavior increased with higher road levels, greater distances to crossing facilities, wider bike lanes, and an increased number of bus stops, subway stations, and leisure and entertainment facilities, while it decreased with a higher number of office commuting facilities. In addition, detour behavior was more prevalent on weekends, during off-peak hours, and under conditions involving physical central lane separation and physical bike lane separation. These findings offer a novel approach for identifying bicycle riding behaviors and analyzing their influencing factors, providing effective technical support for non-motorized traffic management and infrastructure optimization.

为探究自行车绕行行为的分布特征及影响因素，基于共享单车GPS轨迹数据，实现绕行行为的精准判别。考虑出行时间、道路条件、公共交通设施、土地利用性质等多个影响要素，基于CatBoost机器学习算法构建绕行行为影响模型，并通过SHAP可解释机器学习框架解析各因素对绕行行为的影响规律。研究结果显示：CatBoost机器学习模型在绕行行为识别方面表现出较好的效果；随着道路等级、过街设施间距、非机动车道宽度以及公交站、地铁站和休闲娱乐类设施数量的增加，办公通勤类设施数量的减少，绕行行为发生的频率增加；同时，在休息日、非高峰时段、物理中央隔离和物理机非隔离条件下，绕行行为发生频率较大。研究结果为自行车骑行行为判别及影响因素分析提供了一种新的方法，并为非机动车交通管理和设施优化提供了技术支持。

References:

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Memo

Memo:

Received 2024-08-12,Revised 2024-12-20.
Biographies:Bian Yang (1980—), female, doctor, associate professor;Zhao Xiaohua (corresponding author), female, doctor, professor, zhaoxiaohua@bjut.edu.cn.
Foundation item:The National Natural Science Foundation of China(No. 52072012).
Citation:BIAN Yang,YIN Luyao,ZHAO Xiaohua,et al.Research on the discrimination of detour behavior and influencing factors of shared bicycles[J].Journal of Southeast University (English Edition),2025,41(2):215-225.DOI:10.3969/j.issn.1003-7985.2025.02.011.DOI:10.3969/j.issn.1003-7985.2025.02.011

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