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Severity analyses and prediction of e-bikes violated behaviors
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Sheqiang MA1, Yuxiao LU1, Chunjiao DONG2, **, Penghui LI2, Jihui MA2
China Safety Science Journal | 2024, 34(11) : 172 - 178
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China Safety Science Journal | 2024, 34(11): 172-178
Public safety
Severity analyses and prediction of e-bikes violated behaviors
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Sheqiang MA1, Yuxiao LU1, Chunjiao DONG2, **, Penghui LI2, Jihui MA2
Affiliations
  • 1 School of Traffic Management,People's Public Security University of China,Beijing 100038,China
  • 2 Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Ministry of Transport,Beijing Jiaotong University,Beijing 100044,China
Published: 2024-11-28 doi: 10.16265/j.cnki.issn1003-3033.2024.11.0166
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Traffic conflicts caused by illegal riding of e-bikes are a great challenge and negative impact on the safety management and operation efficiency of signalized intersections. In this paper,three indexes including post-encroachment time (PET),time to collision (TTC) and deceleration-to-safety time (DST) were selected from the two aspects of the number of collision objects and motion state. The k-means clustering was adopted to divide the severity of traffic collisions caused by illegal riding of e-bikes into three categories: general,serious and potential collision. Secondly,the Poisson function was used to fit the distribution characteristics of conflict frequency,random variables were introduced to describe the mixed effects of heterogeneity among traffic conflicts,and a prediction model for traffic conflicts of illegal riding e-bikes based on GLMM was built to predict the frequency of traffic conflicts of multi-grade severity. Combined with the data of 996 e-bike traffic conflicts obtained by video investigation,the empirical study shows that the proportion of e-bike traffic conflicts with different severity has nothing to do with the types of violations. The constructed GLMM model is better than generalized linear model (GLM) in fitting the traffic conflict data of illegal cycling e-bikes,and has the best prediction effect on the common conflict frequency. By strengthening the management of e-bike occupation of motor vehicle lanes and the waiting behavior of crossing the line,adding escort officers and adjusting the signal phase,the incidence rate of e-bike conflict can be reduced.

e-bikes  /  generalized linear mixed model (GLMM)  /  violated behaviors  /  severity level  /  conflict prediction
Sheqiang MA, Yuxiao LU, Chunjiao DONG, Penghui LI, Jihui MA. Severity analyses and prediction of e-bikes violated behaviors[J]. China Safety Science Journal, 2024 , 34 (11) : 172 -178 . DOI: 10.16265/j.cnki.issn1003-3033.2024.11.0166
Year 2024 volume 34 Issue 11
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.11.0166
  • Receive Date:2024-05-06
  • Online Date:2025-07-09
  • Published:2024-11-28
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History
  • Received:2024-05-06
  • Revised:2024-08-15
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Affiliations
    1 School of Traffic Management,People's Public Security University of China,Beijing 100038,China
    2 Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Ministry of Transport,Beijing Jiaotong University,Beijing 100044,China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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