Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (2): 413-420.doi: 10.13229/j.cnki.jdxbgxb20210707

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Risk behaviors and influencing factors of cargo electric tricycles

Yi HE1,2(),Chang-xin SUN1,2,Jian-hua PENG3,Chao-zhong WU1,2,Liang JIANG4,Ming MA5   

  1. 1.Intelligent Transportation Systems Research Center,Wuhan University of Technology,Wuhan 430063,China
    2.Engineering Research Center of Waterway and Highway Traffic Safety Control and Equipment,Ministry of Education,Ministry of Education,Wuhan University of Technology,Wuhan 430063,China
    3.China Academy of Transportation Sciences,Ministry of Transport,Beijing 100029,China
    4.China Transport Telecommunications & Information Center,Ministry of Transport,Beijing 100011,China
    5.Department of Transport Services,Ministry of Transport,Beijing 100736,China
  • Received:2021-07-29 Online:2023-02-01 Published:2023-02-28

Abstract:

To explore cargo electric tricycles' risk behavioral characteristics of driving behaviors and its factors, a questionnaire scale of "personality trait-attitude/perception-behaviors" factors of risk behavior was designed and implemented. The data of 906 electric tricycle riders and 280 control group were analyzed, and four risk behavior models of road lapse, errors, speeding and red-light violations were given by structural equation. Based on the information entropy of the sample data, the risk factors were given weight to analyze the influencing mechanism of each factor on the risky riding behavior of cargo electric tricycles. Studies have shown that people who are prone to anger (β=0.332,0.309) and hardly to altruism (β=-0.215,-0.156) are more likely to make risk behaviors of errors and speeding, and people who are prone to anger(β=0.275), have a strong normlessness(β=0.164) and have a poor altruism(β=-0.209) are more likely to make risk behaviors of lapses. People with a high anxiety(β=0.144) and a strong normlessness(β=0.231) are more likely to make risk behavior of red-light running. People with a poor safety attitude are prone to all four risk behaviors. The comprehensive risk scores of the four types of risk behaviors are 0.367, 0.176, 0.321 and 0.136 respetively.

Key words: transportation safety engineering, risk behaviors, structural equation, cargo electric tricycles, cause analysis

CLC Number: 

  • U491.254

Fig.1

Survey sample geographical distribution(top 10)"

Table 1

Main characteristic information of sample"

样本属性组 别电动三轮车电动两轮车
占比 (N=906)占比 (N=280)
性别90.480.7
9.619.3
年龄17~2411.510.4
24~3035.032.7
30~3535.135.6
超过3518.521.2
受教育程度小学2.81.8
中学56.254.3
本科22.629.3
研究生0.60.4
其他17.914.3
月收入低于30006.511.8
3000~500043.240.7
5000~800042.140.7
超过80008.36.8
从业年限0~128.527.5
1~333.337.1
3~522.521.4
超过515.713.9
驾龄0~111.86.8
2~547.648.2
5~1031.334.3
超过109.310.7

每日骑行

距离

0~2022.839.0
20~5040.530.5
50~8021.213.2
80~10012.011.4
超过1003.55.9日工作时长
0~820.317.7
8~1023.927.8
10~1232.839.0
12~1417.49.0超过14
5.66.5过去两年 发生事故
73.682.5
26.417.5

事故严重

程度

未发生事故73.682.5
仅财产损失3.51.8
轻微伤害22.815.4
重伤乃至死亡0.10.4

Table 2

Risk factors scales(attitude factors)"

量表题目及信度均值
E 工作乐趣(α=0.79)2.71
e1 骑车不仅是为了送快件也是为了乐趣2.78
e2 骑电动/摩托车让我很放松2.64
F 骑行自信(α=0.74)3.67
f1 我有信心按时骑到目的地3.79
f2 我有能力处理行驶中的任何危险状况3.56
G 风险感知(α=0.91)3.64
g1 对可能产生严重后果的驾驶操作感到很危险3.62
g2 你是否担心自己在交通事故中受伤3.73
g3 你认为自己发生交通事故的可能性有多少3.57
H 不安全态度(α=0.91)1.62
h1 为赶时间可以在机动车道和非机动车道之间变换1.75
h2 如果工作任务非常紧急,闯红灯是可以的1.51
h3 如果有其他人违反了交通规则,可以跟着一起行动1.46
h4 为了超过缓慢的车流,违反某些交通规则是可以的1.56
h5 为了个人目的可以违反某些交通规则1.43

Table 3

Risk behaviors scales"

量表题目及信度均值
K道路行驶失误(α=0.82)1.61
k1当接近路口或小区门口时不减速1.7
k2骑行期间经常与其他骑行者或行人发生身体接触1.65
k3转弯时不减速1.47
L 道路行驶错误(α=0.87)1.62
l1跟车太近1.5
l2骑车时不带头盔1.73
l3在机动车道上行驶1.58
l4在道路的另一侧逆向行驶1.46
l5骑车时使用手机1.82
M 超速违规(α=0.86)1.53
m1喜欢比其他车辆骑得更快1.56
m2在晚上或者凌晨超速骑行1.37
m3在人流密集的地方也尽可能保持速度1.55
m4任务紧急时会保持最高速度骑行1.62
N 红灯违规(α=0.81)2.3
n1当路上的车流量非常少的时候会忽略红灯2.44
n2等待红灯时感觉很不耐烦1.87
n3如果其他人一起闯红灯,我会跟上去2.58

Fig.2

High-frequency risk behaviors"

Fig.3

Related factors model structure of 4-risk behaviors"

Table 4

Fitting indexes of fours models"

模型χ2/dfGFIAGFICFIRMSEA
失误模型2.8120.9380.9210.96700.045
错误模型2.8560.9310.9140.96320.045
超速模型2.7130.9350.9180.96700.044
红灯模型2.3870.950.9350.97700.039

Fig.4

Path results of the correlation factor model for 4-types of risk behaviors"

Table 5

Factors influencing effect of 4-models"

因素影响效应β
失误K错误L超速M红灯N
焦虑-0.0090.0470.0290.144
愤怒0.3320.2750.3090.052
无规范感0.1640.0400.0930.231
利他-0.209-0.215-0.1560.069
工作乐趣-0.0390.0670.052-0.432
风险感知0.205-0.311-0.1370.729
骑行自信-0.137-0.142-0.151-0.051
不安全态度0.5770.5980.6550.214

Table 6

Factors influencing effect considering difference"

因素考虑风险行为差异的因素影响效应β'权重W
失误K错误L超速M红灯N
综合得分0.3670.1760.3210.136
焦虑-0.0170.0890.0550.2720.236
愤怒0.2320.1920.2160.0360.087
无规范感0.1310.0320.0740.1840.100
利他-0.078-0.080-0.0580.0260.047
工作乐趣-0.1000.1720.133-1.1090.321
风险感知0.205-0.311-0.1370.7290.125
骑行自信-0.046-0.047-0.050-0.0170.042
不安全态度0.1980.2060.2250.0740.043
1 傅志寰, 孙永福, 翁孟勇, 等. 交通强国战略研究[M]. 北京: 人民交通出版社, 2019.
2 严新平, 吴兵, 贺宜, 等. 我国零死亡愿景交通安全理念[J]. 交通信息与安全, 2019(1): 1-6.
Yan Xin-ping, Wu Bing, He Yi, et al. A study of "Version Zero" concept of transportation safety and its implementation strategies in China[J]. Journal of Transport Information and Safety, 2019(1): 1-6.
3 第一物流研究院. 电动三轮车在快递行业使用情况报告[R]. 北京: 现代物流报, 2016.
4 Zhang G, Yau K K W, Chen G. Risk factors associated with traffic violations and accident severity in China[J]. Accident Analysis & Prevention, 2013, 59: 18-25.
5 Vlahogianni E I, Yannis G, Golias J C. Overview of critical risk factors in power-two-wheeler safety[J]. Accident Analysis & Prevention, 2012, 49: 12-22.
6 Johnson P, Brooks C, Savage H. Fatal and Serious Road Crashes Involving Motorcyclists[M]. Australia: Department of Infrastructure, Transport, Regional Development and Local Government, 2008.
7 Zambon F, Hasselberg M. Factors affecting the severity of injuries among young motorcyclists-a Swedish nationwide cohort study[J]. Traffic Injury Prevention, 2006, 7: 143-149.
8 江亮, 贺宜. 电动两轮车风险驾驶行为及事故影响因素分析[J]. 吉林大学学报: 工学版, 2019, 49(4): 1107-1113.
Jiang Liang, He Yi. Risky driving behavior and influencing factors for electric two-wheeler[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1107-1113.
9 Reason J, Manstead A, Stradling S, et al. Errors and violations on the roads: a real distinction?[J]. Ergonomics, 1990, 33(10/11): 1315-1332.
10 Westerman S J, Haigney D. Individual differences in driver stress, error and violation[J]. Personality and Individual Differences, 2000, 29(5): 981-998.
11 Chen C. Personality, safety attitudes and risky driving behaviors—evidence from young Taiwanese motorcyclists[J].Accident Analysis & Prevention, 2009, 41(5): 963-968.
12 Wang C, Xu C, Xia J, et al. The effects of safety knowledge and psychological factors on self-reported risky driving behaviors including group violations for e-bike riders in China[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 56: 344-353.
13 Ulleberg P, Rundmo T. Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers[J]. Safety Science, 2003, 41(5): 427-443.
14 Starkey N J, Isler R B.The role of executive function, personality and attitudes to risks in explaining self-reported driving behaviour in adolescent and adult male drivers[J].Transportation Research Part F:Traffic Psychology and Behaviour,2016,38:127-136.
15 Yao L, Wu C. Traffic safety for electric bike riders in china: attitudes, risk perception, and aberrant riding behaviors[J]. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2314(1): 49-56.
16 Mallia L, Lazuras L, Violani C, et al. Crash risk and aberrant driving behaviors among bus drivers: the role of personality and attitudes towards traffic safety[J]. Accident Analysis & Prevention, 2015, 79: 145-151.
17 Wong J, Chung Y, Huang S. Determinants behind young motorcyclists' risky riding behavior[J]. Accident Analysis & Prevention, 2010, 42(1): 275-281.
18 Johnson J A. Measuring thirty facets of the five factor model with a 120-item public domain inventory: development of the IPIP-NEO-120[J]. Journal of Research in Personality, 2014(51): 78-89.
19 Goldbery L R. The development of markers for the big-five factor structure[J]. Psychological Assessment, 1992, 4(1): 26-42.
20 Anderson J C, Gerbing D W. Structural equation modeling in practice: a review and recommended two-step approach[J]. Psychological Bulletin, 1988, 103(3): 411-423.
21 秦浩, 陈景武. 结构方程模型原理及其应用注意事项[J]. 中国卫生统计, 2006(4): 367-369.
Qin Hao, Chen Jing-wu. Principles and application considerations of structural equation model[J]. Chinese Journal of Health Statistics, 2006(4): 367-369.
22 Hooper D, Coughlan J, Mullen M R. Structural equation modelling: guidelines for determining model fit[J]. Electronic Journal of Business Research Methods, 2008, 1(6): 53-60.
23 Papantoniou P, Antoniou C, Yannis G, et al. Which factors affect accident probability at unexpected incidents? A structural equation model approach[J]. Journal of Transportation Safety & Security, 2019, 11(5): 544-561.
24 吴超仲, 万平, 张晖, 等. 交通从众行为研究——机遇与挑战[J]. 交通信息与安全, 2013, 31(2): 1-5.
Wu Chao Zhong, Wan Ping, Zhang Hui, et al. Traffic conformity behavior research—opportunity and challenge[J]. Journal of Transport Information and Safety, 2013, 31(2): 1-5.
25 董春娇,董黛悦,诸葛承祥,等.电动自行车出行特性及骑行决策行为建模[J].吉林大学学报:工学版,2022, 52(11): 2618-2625.
Dong Chun-jiao, Dong Dai-yue, Cheng-xiang Zhu-ge, et al. Trip characteristics and decision⁃making behaviors modeling of electric bicycles riding[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2618-2625.
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