Severity analyses and prediction of e-bikes violated behaviors

doi:10.16265/j.cnki.issn1003-3033.2024.11.0166

Abstract

Abstract:

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.

Key words: e-bikes, generalized linear mixed model (GLMM), violated behaviors, severity level, conflict prediction

CLC Number:

MA Sheqiang, LU Yuxiao, DONG Chunjiao, LI Penghui, MA Jihui. Severity analyses and prediction of e-bikes violated behaviors[J]. China Safety Science Journal, 2024, 34(11): 172-178.

Figures/Tables 7

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指标	最大值/s	最小值/s	平均值/s	标准差
TTC	7.058	0.131	1.261	1.005
PET	11.948	0.000	1.469	1.137
DST	9.732	0.018	1.873	1.813

分类	影响因素	平均值	标准差	最小值	最大值
违规频率	电动自行车闯红灯频率/ (次·5 min^-1)	0.03	0.09	0	1
	电动自行车占用机动车道频率/(次·5 min^-1)	0.11	0.15	0	0.75
	电动自行车逆向骑行频率/(次·5 min^-1)	0.16	0.22	0	0.97
	电动自行车越线等待频率/(次·5 min^-1)	0.18	0.19	0	0.97
信号交叉口特征	两相位=0,三相位=1, 四相位=2	3.00	0.82	2	4
	白线=0,物理隔离=1	0.42	0.49	0	1
	无=0,有=1	0.50	0.50	0	1
	无=0,有=1	0.50	0.50	0	1
	进口道非机动车道宽度/m	3.47	1.18	2	6
	进口道宽度/m	33.63	10.83	8	48.5
	信号交叉口宽度/m	33.63	10.83	8	48.5
交通流特性	进口道非机动车数量 (pcu·进口道^-1·h^-1)	161.02	79.36	5.52	517.2
	进口道机动车数量/ (pcu·进口道^-1·h^-1)	738.80	353.17	12	1752
	非机动车与机动车交通量比	0.30	0.29	0.03	2.68

冲突类型	GLM			GLMM
冲突类型	-2对数似然	AIC	BIC	-2对数似然	AIC	BIC
潜在	193.878	395.755	410.407	382.707	392.920	411.022
一般	566.783	1 149.566	1 178.870	729.173	731.187	734.808
严重	340.572	687.145	698.134	664.567	691.895	738.185
所有	656.128	1 344.257	1 402.864	639.954	641.969	645.589

自变量	系数	标准误差	t值	p值
截距	-1.856	0.370	-5.043	0.000
占用机动车道频数	0.032	0.011	2.842	0.005
越线等待频数	0.050	0.016	3.226	0.001
信号相位(三/四相位)	0.765	0.170	4.496	0.000
协管员(无/有)	0.820	0.177	4.496	0.000
左转专用相位(无/有)	0.750	0.186	4.029	0.000
信号交叉口宽度/m	0.048	0.008	5.764	0.000
负二项式	0.161	-	-	-