[1] BIAN Yang, REN Bin, ZHAO Xiaohua, LI Yuheng, et al. Spatiotemporal characterization of speeding risk behaviors of shared electric bicycles based on trajectory data [J]. Journal of Southeast University (English Edition), 2025, 41 (4): 512-524. [doi:10.3969/j.issn.1003-7985.2025.04.013]
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Spatiotemporal characterization of speeding risk behaviors of shared electric bicycles based on trajectory data(
)
基于轨迹数据的共享电动自行车超速风险行为时空特征研究
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 41
- Issue:
- 2025 4
- Page:
- 512-524
- Research Field:
- Traffic and Transportation Engineering
- Publishing date:
- 2025-11-30
Info
- Title:
- Spatiotemporal characterization of speeding risk behaviors of shared electric bicycles based on trajectory data
- 基于轨迹数据的共享电动自行车超速风险行为时空特征研究
- Author(s):
- BIAN Yang, REN Bin, ZHAO Xiaohua, LI Yuheng, ZHANG Xiaolong
- College of Urban Transportation, Beijing University of Technology, Beijing 100124, China
- Keywords:
- shared electric bicycle; speeding behavior; trajectory data mining; spatiotemporal hotspot analysis; traffic risk management
- PACS:
- U491
- DOI:
- 10.3969/j.issn.1003-7985.2025.04.013
- Abstract:
- To reduce the risk of traffic accidents significantly caused by the speeding behavior of electric bicycles, this study focuses on the Beijing Yizhuang Economic and Technological Development Zone. This work relies on high-precision shared electric bicycle Global Positioning System trajectory data, integrating a spatiotemporal analysis model and geographic information system (GIS) technology to explore the spatial and temporal variability law and formation mechanism of speeding behavior. Through data preprocessing, speeding events are identified, and weekday features are extracted. Four periods are identified: morning peak, midday minipeak, evening peak, and nighttime flat peak. Using the GIS platform, global spatial autocorrelation and local clustering analysis are conducted to identify the spatial clustering characteristics of speeding behaviors and hotspot areas. The coldspot and hotspot patterns of speeding events and the dynamic trajectories of their evolution are analyzed using spatiotemporal cube technology. The results show that speeding behaviors are strongly correlated with the commuting peak in time and spatially concentrated in the intersections of urban main roads, the periphery of commercial complexes, and industrial parks, with a diffusion tendency. The results of this study provide novel insights into the research and analysis of the spatial and temporal characteristics of speeding risk behaviors of electric bicycles and effective technical support for nonmotorized traffic safety management.
- 为改善因电动自行车超速行为而显著增加的交通事故风险，本研究聚焦北京市亦庄经济技术开发区，依托高精度共享电动自行车GPS轨迹数据，集成时空分析模型与地理信息系统技术，探究超速行为的时空分异规律及形成机制。通过数据预处理，识别超速事件并提取工作日特征，划分早高峰、午间小高峰、晚高峰、夜间平峰4个时段；基于地理信息系统平台，进行全局空间自相关与局部聚类分析，识别超速行为的空间集聚特性及热点区域；利用时空立方体技术，分析超速事件的冷热点模式与动态演化轨迹。结果表明，超速行为在时间上与通勤高峰强相关，在空间上集中在城市主干道交叉口、商业综合体周边及工业园区，且存在扩散趋势。研究结果为电动自行车超速风险骑行行为时空特征研究分析提供了新的方法，并为非机动车交通安全管理提供了有效的技术支持。
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Memo
- Memo:
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Received 2025-04-27,Revised 2025-08-01.
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,REN Bin,ZHAO Xiaohua,et al.Spatiotemporal characterization of speeding risk behaviors of shared electric bicycles based on trajectory data[J].Journal of Southeast University (English Edition),2025,41(4):512-524.DOI:10.3969/j.issn.1003-7985.2025.04.013.DOI:10.3969/j.issn.1003-7985.2025.04.013