电动两轮车风险驾驶行为及事故影响因素分析

doi:10.13229/j.cnki.jdxbgxb20180189

Abstract

Abstract:

In order to explore the causes and influencing factors of risky driving behavior of electric two-wheeled vehicles, a questionnaire survey was conducted among 183 electric two-wheeled cyclists. After exploratory factor analysis (EFA), a 6-factor structural model was established. These factors include risk driving behavior, emotion State, altruism, risk perception, driving self-confidence and risk awareness. The relationship between statistical information, the 6 factors and traffic accidents were studied to establish a traffic accident prediction model based on Logistic regression. This model was validated by the Factor Analysis (CFA). Research shows that daily mileage, risk driving behavior, working hours, risk awareness are important parameters affecting traffic accidents.

Key words: engineering of communication and transportation system, electric two?wheeler, driving behavior, road accident, influencing factors, cause analysis

CLC Number:

U121

Liang JIANG,Yi HE. Risky driving behavior and influencing factors analysis for electric two⁃wheeler[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1107-1113.

Figures/Tables 5

Table 1

Fig.1

Table 2

Table 3

Table 4

References 17

1	全球电动车网. 2016年电动三轮产业发展大数据[EB/OL]. [2017⁃02⁃13]. http:∥.
2	YuS, YangZ, YuB. Air express network design based on express path choices–Chinese case study[J]. Journal of Air Transport Management, 2017, 61: 73⁃80.
3	高继东, 曾必强, 彭伟. 电动自行车与轿车碰撞中骑车人的伤害特征[J]. 吉林大学学报: 工学版, 2016, 46(6): 1786⁃1791.
	GaoJi⁃dong, ZengBi⁃qiang, PengWei. Cyclist injury in collision between car and electric bicycle[J]. Journal of Jilin University(Engineering and Technology Edition), 2016, 46(6): 1786⁃1791.
4	刘东, 韩凤春, 陈永胜. 灯控路口非机动车交通流运行特性研究[J]. 中国人民公安大学学报: 自然科学版 2004, 10(2): 89⁃92.
	LiuDong, HanFeng⁃chun, ChenYong⁃sheng. Study on traffic flow characteristics of non⁃motorized vehicle at light control intersection[J]. Journal of Chinese People’s Public Security University(Science and Technology), 2004, 10(2): 89⁃92.
5	XuJ, ChenL, WangD, et al. The backbone transportation network optimization problem of air express based on scale economies[J]. Advances in Transportation Studies, 2016, 3:61⁃74.
6	WaltonD, BuchananJ. Motorcycle and scooter speeds approaching urban intersections[J]. Accident Analysis and Prevention, 2012, 48: 335⁃340.
7	北京青年报. 去年北京电动二轮车共发生交通事故31404起[EB/OL]. [2016⁃04⁃05]. http:∥news.sohu.com/20160405/n443301797.shtml.
8	重庆日报. 重庆电动两轮车使用管理的调查该当如何管住？[EB/OL]. [2017⁃06⁃30]. http:∥cq.qq.com/a/20170630/015918.
	htm.
9	ReasonJ, MansteadA, StranlingS, et al. Errors and violations on the roads: a real distinction?[J]. Ergonomics, 1990, 33(10/11): 1315⁃1332.
10	AbergL, RimmoP A. Dimensions of aberrant driver behavior[J]. Ergonomics, 1998, 41(1): 39⁃56.
11	XieCheng⁃qiu, ParkerD. A social psychological approach to driving violations in two Chinese cities[J]. Transportation Research Part F: Psychology and Behaviour, 2002, 5(4): 293⁃308.
12	白云，石京. 北京市驾驶行为与影响因素分析[J]. 交通信息与安全, 2010, 28(2): 114⁃119.
	BaiYun, ShiJing. Driving behavior and influential factors study in Beijing[J]. Journal of Transportation Information and Safety, 2010, 28(2): 114⁃119.
13	刘霞. 驾驶人驾龄与交通事故相关性研究[D]. 西安：长安大学汽车学院, 2016.
	LiuXia. Study on correlation between driving experience and traffic accidents[D]. Xi’an: School of Automobile, Chang'an University, 2016.
14	TsengC M. Social⁃demographics, driving experience and yearly driving distance in relation to a tour bus driver's at⁃fault accident risk[J]. Tourism Management, 2012, 33(4): 910⁃915.
15	NohK H, RahC K, YoonY S, et al. Experimental approach to developing human driver models considering driver’s human factors[J]. International Journal of Automotive Technology, 2014, 15(4): 655⁃666.
16	LiXiao⁃ping, XieLi⁃juan, XuLan. Research on classification and restruction of express service quality elements based on Kano model[C]∥The 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, China,2016: 1⁃6.
17	MuhammadAlam,JoaquimFerreira,JoséFonseca. Intelligent Transportation Systems: Dependable Vehicular Communications for Improved Road Safety[M]. Berlin: Springer, 2016.

Related Articles 15

[1]	Zhi⁃hui LI,Tao ZHONG,Yong⁃hua ZHAO,Yong⁃li HU,Hai⁃tao LI,Jing⁃wei ZHAO. Pedestrian tracking algorithm for autonomous driving [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 680-687.
[2]	Xiao⁃qin LUO,Dian⁃hai WANG,Sheng JIN. Traffic signal actuated control at isolated intersections for heterogeneous traffic [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 695-704.
[3]	QU Da-yi,YANG Jing-ru,BING Qi-chun,WANG Wu-lin,ZHOU Jing-chun. Arterial traffic offset optimization based on queue characteristics at adjacent intersections [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1685-1693.
[4]	LI Zhi-hui, HU Yong-li, ZHAO Yong-hua, MA Jia-lei, LI Hai-tao, ZHONG Tao, YANG Shao-hui. Locating moving pedestrian from running vehicle [J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[5]	SHAO Sai, BI Jun, GUAN Wei. Electric vehicle routing problem with charging and dynamic customer demands [J]. 吉林大学学报(工学版), 2017, 47(6): 1688-1695.
[6]	SUN Zong-yuan, FANG Shou-en. Hierarchical clustering algorithm of moving vehicle trajectories in entrances and exits freeway [J]. 吉林大学学报(工学版), 2017, 47(6): 1696-1702.
[7]	ZHANG Zhe, JIA Li-min, QIN Yong, YUN Ting. Equalization-based feedback control model of pedestrian counter flow [J]. 吉林大学学报(工学版), 2017, 47(6): 1728-1737.
[8]	LI Zhi-hui, WANG Kun-wei, SONG Xian-min, LIU Xin-shan, SHEN Yao, LUO Rui-qi. Roundabout travel time prediction based on characteristics of lane choosing [J]. 吉林大学学报(工学版), 2017, 47(5): 1411-1419.
[9]	LI Xian-sheng, MENG Fan-song, ZHENG Xuan-lian, REN Yuan-yuan, YAN Jia-hui. Driver's visual characteristics based on stress response [J]. 吉林大学学报(工学版), 2017, 47(5): 1403-1410.
[10]	LI Ye, WANG Wei, XING Lu, WANG Hao, DONG Chang-yin. Improving traffic efficiency of highway by integration of adaptive cruise control and variable speed limit control [J]. 吉林大学学报(工学版), 2017, 47(5): 1420-1425.
[11]	WAN Ping, WU Chao-zhong, LIN Ying-zi, MA Xiao-feng. Driving anger detection based on multivariate time series features of driving behavior [J]. 吉林大学学报(工学版), 2017, 47(5): 1426-1435.
[12]	JIANG Pan, YANG Jia-qi, FANG Rui-wei. Bi-level programming model for optimization of urban agglomeration comprehensive transportation corridor layout [J]. 吉林大学学报(工学版), 2017, 47(4): 1061-1067.
[13]	WANG Lei, LIU Zhao, LIU Yang. Shift quality evaluation based on human response spectrum analysis [J]. 吉林大学学报(工学版), 2017, 47(3): 725-730.
[14]	ZHAO Xue-yu, YANG Jia-qi, PENG Ya-mei. Competitive and cooperative relationship evolution mechanism between urban rail transit and traditional bus [J]. 吉林大学学报(工学版), 2017, 47(3): 756-764.
[15]	WANG Hai-wei, WEN Hui-ying, LIU Min. Experimental evaluation of nighttime driver's physiological characteristics in driving simulator [J]. 吉林大学学报(工学版), 2017, 47(2): 420-428.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

检验方法		数值
KMO检验		0.849
巴特利特球度检验	近似χ²	4149.884
	df	741
	Sig.	0.000

序号	问卷项目	因子
序号	问卷项目	1	2	3	4	5	6
1	日常工作强度	0.871
2	平时心态很放松	0.873
3	对已经发生的事情不担心	0.870
4	很容易适应新环境	0.873
5	担心坏事发生	0.871
6	担心所有的事情	0.869
7	遇事能保持冷静	0.876
8	很少抱怨	0.871
9	易发脾气	0.870
10	容易失落	0.868
11	喜欢刺激		0.868
12	喜欢尝试新事物		0.871
13	做事方式直接		0.871
14	做事不考虑后果		0.868
15	从不尝试滑翔和蹦极等危险运动		0.871
16	乐于帮助别人			0.873
17	人缘好，受欢迎			0.874
18	乐于关心其他人			0.873
19	与别人相处有困难			0.870
20	不喜欢为他人的事情浪费时间			0.871
21	不喜欢嘈杂热闹的音乐			0.870
22	22.即便在陌生道路上行驶，我也可以处理任何突发状况				0.872
23	我有能力处理行驶中的任何危险状况				0.873
24	与两侧高速行驶的车辆并排骑行				0.866
25	与前面车辆距离过近以至紧急情况难以停下来				0.867
26	不注意观察，骑车从支路直接插到主路上				0.867
27	拐弯速度过快以至失控					0.867
28	骑车不仅是为了送快件也是为了乐趣					0.870
29	骑（电动）摩托车让我很放松					0.869
30	为图方便，认为可以逆向行驶					0.865
31	认为如果车技好，可以超速					0.865
32	认为道路条件好，可以超速					0.865
33	直行或转弯时，不看后视镜观察后方情况						0.866
34	骑车转弯时不看红绿灯						0.867
35	为赶时间，在狭窄的车辆缝隙之间钻行						0.865
36	喜欢比其他车辆骑得更快						0.867
37	在晚上或者凌晨超速骑行						0.866
38	酒后骑车						0.868
39	骑车闯红灯						0.866

序号		1	2	3	4	5	6	7	8	9	10
1	年龄
2	从业年限	0.227
3	日均骑行次数	0.073	0.204
4	日均里程	0.045	0.068	0.009
5	工作时长	0.025	0.010	0.085	0.016
6	风险驾驶行为	0.045	0.059	0.037	0.030	0.136
7	正面情绪状态	0.016	0.044	0.110	0.117	0.085	0.089
8	利他主义	0.045	0.004	0.086	0.077	0.136	0.036	0.046
9	风险感知能力	0.041	0.019	0.027	0.007	0.034	0.047	0.085^*	0.024
10	驾驶自信	0.004	0.080	0.083	0.041	0.077	0.059	0.022	0.014	0.014
11	冒险意识	0.036	0.074	0.064	0.111	0.028	0.090	0.013	0.035	0.047	0.020

变量	B	Wals	Sig.	Exp (B)
风险驾驶行为	0.947	6.582	0.010	2.579
正面情绪状态	1.058	4.316	0.038	2.880
利他主义	-0.367	0.685	0.408	0.693
风险感知能力	0.326	0.886	0.347	1.386
驾驶自信	-0.375	0.731	0.393	0.687
冒险意识	0.055	0.019	0.891	1.056
年龄	0.053	0.731	0.393	1.055
从业年限	0.007	0.010	0.920	1.007
日均送快件数	0.000	0.003	0.956	1.000
日均里程	-0.043	0.836	0.360	0.958
工作时长	0.094	0.147	0.701	1.098

Risky driving behavior and influencing factors analysis for electric two⁃wheeler

RICH HTML

PDF (PC)