Spatiotemporal characterization of speeding risk behaviors of shared electric bicycles based on trajectory data

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.

Keywords

shared electric bicycle / speeding behavior / trajectory data mining / spatiotemporal hotspot analysis / traffic risk management

References

[1] SHU S N, BIAN Y, RONG J, et al. Process of phased shift from car to bicycle travel based on the transtheoretical model[J]. Journal of Southeast University (Natural Science Edition), 2018, 48(2): 373-380. (in Chinese)

[2] ZHANG X L, HUANG J L, BIAN Y, et al. Analysis of factors influencing the severity of injury in shared electric bicycle riders[J]. Journal of Southeast University(English Edition), 2023, 39(3): 284-291.

[3] WANG C, KOU S Y, SONG Y C. Identify risk pattern of E-bike riders in China based on machine learning framework[J]. Entropy, 2019, 21(11): 1084.

[4] XU C, GUO H F, XU L, et al. Speeding behavior and speed limits for heterogeneous bicycle flow[J]. Traffic Injury Prevention, 2019, 20(7): 759-763.

[5] ZHANG X L, BIAN Y, ZHAO X H, et al. Analysis of factors influencing the speeding risk of shared e-bike roadway[J]. Journal of Southeast University (Natural Science Edition), 2024, 54(1): 214-223. (in Chinese)

[6] MA C X, ZHOU J B, YANG D, et al. Research on the relationship between the individual characteristics of electric bike riders and illegal speeding behavior: A questionnaire-based study[J]. Sustainability, 2020, 12(3): 799.

[7] YANG X Y, ZHANG Y T, WANG H R, et al. Psychological factors predicting speeding riding behavior in electric bicycles[J]. Transportation Medicine, 2022, 36(4): 356-360. (in Chinese)

[8] KRAAK M. The space-time cube revisited from a geovisualization perspective[C]// Proceedings of the 21st International Cartographic Conference. Durban, South Africa, 2003: 1988-1996.

[9] GATALSKY P, AND RIENKO N, AND RIENKO G, Interactive analysis of event data using space-time cube[C]// Proceedings of the Eighth International Conference on Information Visualization. Wuhan, China, 2004: 145-152.

[10] CHENG S F, ZHANG B B, PENG P, et al. Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China[J]. Journal of Cleaner Production, 2020, 244: 118654.

[11] ZHU Y L, JING C F, FU J Y, et al. Analysis of space-time pattern of robbery crime based on space-time cube[J]. Science of Surveying and Mapping, 2019, 44(9): 132-138.

[12] WU Q. Research on spatio-temporal characterization and severity modeling of taxi traffic violations[D]. Nanchang: East China Jiaotong University, 2023. (in Chinese)

[13] ZHAO Z Y, HUANG Y G, WU S, et al. A feature recognition method for urban traffic violations based on spatio-temporal hotspot analysis[J]. Journal of Earth Information Science, 2022, 24(7):1312-1325. (in Chinese)

[14] WANG C W, HE J, YAN X T, et al. Temporal-spatial evolution analysis of severe traffic violations using three functional forms of models considering the diurnal variation of meteorology[J]. Accident Analysis and Prevention, 2022, 174:106731.

[15] ACHU L A, AJU D C, SURESH V, et al. Spatio-temporal analysis of road accident incidents and delineation of hotspots using geospatial tools in Thrissur District, Kerala, India[J]. KN-Journal of Cartography and Geographic Informatio, 2019, 69(5): 255-265.

[16] HONG A D. Spatio-temporal pattern mining and analysis of traffic congestion points based on spatio-temporal cube[D]. Chengdu: Southwest Jiaotong University, 2017. (in Chinese)

[17] YUAN J, ZHENG Y, ZHANG C Y, et al. An interactive-voting based map matching algorithm[C]// 2010 Eleventh International Conference on Mobile Data Management. Kansas City, MO, USA, 2010: 43-52.

[18] LIU H Y, FU C Y, JIANG C Z, et al. Bayesian hierarchical spatial count modeling of taxi speeding events based on GPS trajectory data[J]. PLoS One, 2020, 15(11): e0241860.

[19] SEO H, SHIN J, KIM K H, et al. Driving risk assessment using non-negative matrix factorization with driving behavior records[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 20398-20412.

[20] MA G Q, CHEN X M. Study on complex network of chinese industries, measurement of regional influence and evolution of spatio-temporal dynamics[J]. Modern Management Science, 2023(6): 41-54. (in Chinese)

[21] LI Y. An exploratory study of spatial lag regression modeling in the distribution of pulmonary tuberculosis[D]. Wuhan: Wuhan Institute of Technology, 2013. (in Chinese)

[22] WANG Y F. Local linear kernel estimation of nonparametric spatial lag model and its application to air quality evaluation in Shandong Province, China[D]. Ganzhou: Jiangxi University of Science and Technology, 2023. (in Chinese)

[23] YIN J H, LI X Q. Spatio-temporal patterns in pedestrian crashes and their determining factors: Application of a space-time cube analysis model[J]. Accident Analysis and Prevention, 2021, 161:106291.

[24] TORSTEN H. What about people in regional science[J]. Papers in Regional Science, 2005, 24(1):7-24.

[25] WU P J, MENG X H, CAO M D. Identification of urban traffic accident frequency points and mining of spatio-temporal patterns[J]. Chinese Journal of Safety Science, 2020, 30(11):127-133.

[26] ZHANG X L, ZHAO X H, BIAN Y, et al. Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach[J]. Accident Analysis and Prevention, 2024, 207: 207107755.

[27] GUO M Q, GOU C D, TAN S C, et al. Spatiotemporal characteristics and factors influencing the cycling behavior of shared electric bike use in urban plateau regions[J]. Sustainability, 2024, 16(15):6570-6570.

Funding

The National Natural Science Foundation of China(52072012)