电动载货三轮车风险行为及影响因素分析

doi:10.13229/j.cnki.jdxbgxb20210707

吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (2): 413-420.doi: 10.13229/j.cnki.jdxbgxb20210707

• 交通运输工程·土木工程 • 上一篇下一篇

电动载货三轮车风险行为及影响因素分析

贺宜^1,²(),孙昌鑫^1,²,彭建华³,吴超仲^1,²,江亮⁴,马明⁵

^1.武汉理工大学智能交通系统研究中心，武汉 430063
^2.武汉理工大学水路公路交通安全控制与装备教育部工程研究中心，武汉 430063
^3.交通运输部科学研究院，北京 100029
^4.交通运输部中国交通通信信息中心，北京 100011
^5.交通运输部运输服务司，北京 100736

收稿日期:2021-07-29 出版日期:2023-02-01 发布日期:2023-02-28
作者简介:贺宜（1986-），男，副研究员，博士. 研究方向：智能交通，交通安全. E-mail： heyi@whut.edu.cn
基金资助:
国家自然科学基金项目(52072292)

Risk behaviors and influencing factors of cargo electric tricycles

Yi HE^1,²(),Chang-xin SUN^1,²,Jian-hua PENG³,Chao-zhong WU^1,²,Liang JIANG⁴,Ming MA⁵

^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

摘要/Abstract

摘要：

为探究电动载货三轮车群体风险驾驶行为的行为特征及其影响因素，设计并实施了“个性特质-态度/感知-行为”风险行为影响因素问卷量表，分析了906名电动载货三轮车骑行者和280名对照组，数据采用结构方程模型建立了道路行驶失误、道路行驶错误、超速违规和红灯违规4类风险行为模型，基于样本数据的信息熵对风险因素二次赋权，分析各因素对电动载货三轮车骑行风险行为的影响机制。结果表明：容易愤怒（β=0.332，0.309）且利他性差（β=-0.215，-0.156）者易发生行驶错误和超速违规的行为；容易愤怒（β=0.275），无规范感较强（β=0.164）且利他性差（β=-0.209）者容易发生行驶失误类风险行为；焦虑程度高（β=0.144）且无规范感强（β=0.231）者容易发生红灯违规类风险行为；安全态度差者容易发生所有风险行为。失误、错误、超速、红灯4类风险行为的综合风险得分分别为0.367、0.176、0.321、0.136。

关键词: 交通运输安全工程, 风险行为, 结构方程模型, 电动载货三轮车, 致因分析

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

中图分类号:

U491.254

贺宜,孙昌鑫,彭建华,吴超仲,江亮,马明. 电动载货三轮车风险行为及影响因素分析[J]. 吉林大学学报(工学版), 2023, 53(2): 413-420.

Yi HE,Chang-xin SUN,Jian-hua PENG,Chao-zhong WU,Liang JIANG,Ming MA. Risk behaviors and influencing factors of cargo electric tricycles[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 413-420.

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样本属性	组别	电动三轮车	电动两轮车
样本属性	组别	占比（N=906）	占比（N=280）
性别	男	90.4	80.7
性别	女	9.6	19.3
年龄	17~24	11.5	10.4
	24~30	35.0	32.7
	30~35	35.1	35.6
	超过35	18.5	21.2
受教育程度	小学	2.8	1.8
	中学	56.2	54.3
	本科	22.6	29.3
	研究生	0.6	0.4
	其他	17.9	14.3
月收入	低于3000	6.5	11.8
	3000~5000	43.2	40.7
	5000~8000	42.1	40.7
	超过8000	8.3	6.8
从业年限	0~1	28.5	27.5
	1~3	33.3	37.1
	3~5	22.5	21.4
	超过5	15.7	13.9
驾龄	0~1	11.8	6.8
	2~5	47.6	48.2
	5~10	31.3	34.3
	超过10	9.3	10.7
每日骑行距离	0~20	22.8	39.0
	20~50	40.5	30.5
	50~80	21.2	13.2
	80~100	12.0	11.4
超过100	3.5	5.9	日工作时长
	0~8	20.3	17.7
	8~10	23.9	27.8
	10~12	32.8	39.0
12~14	17.4	9.0	超过14
	5.6	6.5	过去两年发生事故
	是	73.6	82.5
	否	26.4	17.5
事故严重程度	未发生事故	73.6	82.5
	仅财产损失	3.5	1.8
	轻微伤害	22.8	15.4
	重伤乃至死亡	0.1	0.4

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

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

模型	χ²/df	GFI	AGFI	CFI	RMSEA
失误模型	2.812	0.938	0.921	0.9670	0.045
错误模型	2.856	0.931	0.914	0.9632	0.045
超速模型	2.713	0.935	0.918	0.9670	0.044
红灯模型	2.387	0.95	0.935	0.9770	0.039

因素	影响效应β
因素	失误K	错误L	超速M	红灯N
焦虑	-0.009	0.047	0.029	0.144
愤怒	0.332	0.275	0.309	0.052
无规范感	0.164	0.040	0.093	0.231
利他	-0.209	-0.215	-0.156	0.069
工作乐趣	-0.039	0.067	0.052	-0.432
风险感知	0.205	-0.311	-0.137	0.729
骑行自信	-0.137	-0.142	-0.151	-0.051
不安全态度	0.577	0.598	0.655	0.214

电动载货三轮车风险行为及影响因素分析

Risk behaviors and influencing factors of cargo electric tricycles

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因素	考虑风险行为差异的因素影响效应β'				权重W
因素	失误K	错误L	超速M	红灯N	权重W
综合得分	0.367	0.176	0.321	0.136
焦虑	-0.017	0.089	0.055	0.272	0.236
愤怒	0.232	0.192	0.216	0.036	0.087
无规范感	0.131	0.032	0.074	0.184	0.100
利他	-0.078	-0.080	-0.058	0.026	0.047
工作乐趣	-0.100	0.172	0.133	-1.109	0.321
风险感知	0.205	-0.311	-0.137	0.729	0.125
骑行自信	-0.046	-0.047	-0.050	-0.017	0.042
不安全态度	0.198	0.206	0.225	0.074	0.043