基于强化敏感性理论的电动自行车风险骑行行为影响因素

汤天培; 陈丰; 郭赟韬; 朱森来

doi:10.3963/j.jssn.1674-4861.2021.03.004

基于强化敏感性理论的电动自行车风险骑行行为影响因素

doi: 10.3963/j.jssn.1674-4861.2021.03.004

1.
南通大学交通与土木工程学院江苏南通 226019
2.
同济大学道路与交通工程教育部重点实验室上海 201804

基金项目:

江苏省社会科学基金项目 20GLC015

江苏省自然科学基金项目 BK20190926

浙江省重点研发计划项目 2021C01011

江苏省高等学校自然科学研究面上项目 19KJB580003

中央高校基本科研业务费专项资金项目 22120210251

中央高校基本科研业务费专项资金项目 22120210252

详细信息

作者简介:
汤天培（1987—），博士，副教授.研究方向：交通安全、交通行为.E-mail:tangtianpei@ntu.edu.cn

通讯作者:
陈丰（1982—），博士，副教授.研究方向：交通安全、智能网联汽车、风工程等.E-mail: fengchen@tongji.edu.cn

中图分类号: U491
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出版历程
- 收稿日期: 2020-10-18

Influencing Factors of Electrical Bikes'Risky Riding Behaviors Based on Reinforcement Sensitivity Theory

1.
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, Jiangsu, China
2.
The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

摘要

摘要: 从交通管理的奖惩机制角度，探究电动自行车骑行人的奖惩反应性对其风险骑行行为的影响机理。采用改进强化敏感性理论构建风险骑行行为的心理认知模型。在改进强化敏感性理论框架下，引入风险感知和风险骑行意向，同时考虑性别、年龄和骑行次数的影响，采用结构方程模型评估影响风险骑行行为的主要心理因素。通过问卷调查，共获取402个有效样本。研究结果表明：①修正后的心理认知模型对数据的适配性良好（χ²/df=1.343，RMSEA=0.029），能解释风险骑行行为48%的变异；②惩罚敏感性和奖励敏感性显著影响风险骑行行为，且奖励敏感性的影响程度更大；③风险感知和风险骑行意向显著影响风险骑行行为；④性别显著影响惩罚敏感性和奖励敏感性，且通过二者间接显著影响风险骑行行为；而年龄、骑行次数对各变量的影响均不显著。
- 交通安全 /
- 电动自行车 /
- 风险骑行行为 /
- 强化敏感性 /
- 行为心理认知
Abstract: From a traffic management perspective towards the reward and punishment strategies, the work studies the influence mechanisms of the reward and punishment responses of electric bike riders on their risky riding behaviors.A psychological cognitive model for risky riding behaviors is developed based on the revised reinforcement sensitivity.Perceived risk and risky riding intention are incorporated into the proposed framework, accounting for the potential impacts of gender, age, and riding frequency.The structural equation model is used to identify key psychological factors influencing risky riding behaviors with the self-reported survey data of 402 valid samples.The model-estimation results are as follows: ①The revised psychological cognitive model fits the data well(χ²/df=1.343, and RMSEA=0.029)and can explain 48%of the variance in risky riding behaviors.②Punishment sensitivity and reward sensitivity significantly affect risky riding behaviors, with the stronger influence of the latter.③Perceived risk and risky riding intention statistically affect risky riding behaviors.④Gender directly affects punishment sensitivity and rewardsensitivity and indirectly affects the risky riding behaviors via both variables.The influence of age and riding frequency on each variable is not significant.
- traffic safety /
- electrical bike /
- risky riding behaviors /
- reinforcement sensitivity /
- psychological cognition of behaviors

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图 1 修正后模型拟合结果

Figure 1. Fitting results of the modified model

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表 1 变量描述性统计与相关关系

Table 1. Descriptive statistics and correlation coefficient for variables

	题项								均值	方差	Cronbach's α系数
变量	1	2	3	4	5	6	7	8	均值	方差	Cronbach's α系数
1.性别	1	0.020	-0.026	0.223**	-0.142**	0.056	-0.029	0.008	1.351	0.478
2.年龄		1	-0.045	-0.031	0.071	0.025	-0.053	0.017	2.552	1.096
3.骑行次数			1	0.014	0.012	0.079	-0.003	-0.003	2.192	0.790
4.惩罚敏感性				1	-0.523**	0.154**	-0.062	-0.214**	2.381	0.669	0.822
5.奖励敏感性					1	-0.091	0.150**	0.316**	2.595	0.531	0.794
6.风险感知						1	-0.255**	-0.346**	4.044	0.826	0.851
7.风险骑行意向							1	0.611**	1.528	0.711	0.911
8.风险骑行行为								1	1.738	0.700	0.887
注：**表示p < 0.01

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表 2 模型修正前后的适配指标

Table 2. Degree-of-fit indices for original and modified models

适配指标	χ²/df	RMESA	GFI	NFI	IFI	CFI	TLI	AGFI
参考标准	< 3	< 0.08	>0.9	>0.9	>0.9	>0.9	>0.9	>0.9
初始模型	30.224	0.270	0.939	0.749	0.755	0.742	0.804	0.448
修正后模型	1.343	0.029	0.994	0.980	0.995	0.995	0.979	0.970

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表 3 修正后模型路径检验

Table 3. Path-testing results for the modified model

路径假设	模型路径			非标准化路径系数	S.E.	C.R.	P	标准化路径系数	假设是否成立
H₁	惩罚敏感性	→	风险感知	0.208	0.072	2.882	0.004	0.168	假设成立
H₂	惩罚敏感性	→	风险骑行意向	-0.020	0.060	-0.335	0.738	-0.019	假设不成立
H₃	惩罚敏感性	→	风险骑行行为	-0.124	0.044	-2.785	0.005	-0.118	假设成立
H₄	奖励敏感性	→	风险感知	-0.018	0.090	-0.206	0.837	-0.012	假设不成立
H₅	奖励敏感性	→	风险骑行意向	0.218	0.075	2.918	0.004	0.163	假设成立
H₆	奖励敏感性	→	风险骑行行为	0.265	0.056	4.724	***	0.202	假设成立
H₇	风险感知	→	风险骑行意向	-0.229	0.042	-5.506	***	-0.266	假设成立
H₈	风险感知	→	风险骑行行为	-0.212	0.032	-6.628	***	-0.251	假设成立
H₉	风险骑行意向	→	风险骑行行为	0.500	0.037	13.467	***	0.508	假设成立
H₁₀	性别	→	惩罚敏感性	0.209	0.059	3.526	***	0.149	假设成立
H₁₁	性别	→	奖励敏感性	-0.165	0.055	-3.014	0.003	-0.148	假设成立
H₁₂	性别	→	风险感知	0.168	0.087	1.932	0.053	0.097	假设不成立
H₁₆	年龄	→	奖励敏感性	0.039	0.024	1.618	0.106	0.080	假设不成立
H₁₇	年龄	→	风险感知	0.016	0.037	0.420	0.675	0.021	假设不成立
H₁₈	年龄	→	风险骑行意向	-0.038	0.031	-1.229	0.219	-0.059	假设不成立
H₁₉	年龄	→	风险骑行行为	0.021	0.023	0.890	0.373	0.032	假设不成立
H₂₁	骑行次数	→	奖励敏感性	0.007	0.033	0.218	0.827	0.011	假设不成立
H₂₂	骑行次数	→	风险感知	0.089	0.051	1.728	0.084	0.085	假设不成立
H₂₃	骑行次数	→	风险骑行意向	0.012	0.043	0.283	0.777	0.013	假设不成立
H₂₄	骑行次数	→	风险骑行行为	0.019	0.032	0.586	0.558	0.021	假设不成立
H₂₅	奖励敏感性	→	惩罚敏感性	-0.631	0.053	-11.830	***	-0.501	假设成立
注：***表示P＜0.001

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参考文献(32)

[1]	公安部交通管理局. 2017年中华人民共和国道路交通事故统计年报[R]. 无锡: 公安部交通管理局, 2018. Traffic Management Bureau of the Ministry of Public Security. The people's republic of China road traffic accident annual statistic report[R]. Wuxi: Traffic Management Bureau of the Ministry of Public Security, 2018. (in Chinese).
[2]	CHEN Qiang, DAI Bing. Characteristics and casualty analysis of two-wheeler accidents in China, data source: The China in-depth accident study(CIDAS)[J]. SAE Technical Paper, 2018(1): 1-10. http://www.researchgate.net/publication/324409743_Characteristics_and_Casualty_Analysis_of_Two-_Wheeler_Accidents_in_China_Data_Source_The_China_In-Depth_Accident_Study_CIDAS
[3]	WU Changxu, YAO Lin, ZHANG Kan. The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study[J]. Accident Analysis & Prevention, 2012, 49(11): 186-192. http://www.ncbi.nlm.nih.gov/pubmed/23036395
[4]	PAI C W, JOU R C. Cyclists'red-light running behaviors: An examination of risk taking, opportunistic, and law-obeying behaviors[J]. Accident Analysis & Prevention, 2014(62): 191-198. http://www.ncbi.nlm.nih.gov/pubmed/24172086
[5]	李英帅, 张旭, 王卫杰, 等. 基于随机森林的电动自行车骑行者事故伤害程度影响因素分析[J]. 交通运输系统工程与信息, 2021, 21(1): 196-200. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202101032.htm LI Yingshuai, ZHANG Xu, WANG Weijie, et al. Factors affecting electric bicycle rider injury in accident based on random Forest model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1): 196-200. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202101032.htm
[6]	何庆, 马建菲, 李洋, 等. 基于122警情的电动自行车交通事故特征研究[J]. 交通信息与安全, 2018, 36(5): 132-138. doi: 10.3963/j.issn.1674-4861.2018.05.017 HE Qing, MA Jianfei, LI Yang, et al. A study of characteristics of road accidents involve electric bicycles based on 122 emergency number[J]. Journal of Transport Information and Safety, 2018, 36(5): 132-138. (in Chinese). doi: 10.3963/j.issn.1674-4861.2018.05.017
[7]	YANG Xiaobao, HUAN Mei, ABDEL-ATY M, et al. A hazard-based duration model for analyzing crossing behavior of cyclists and electric bike riders at signalized intersections[J]. Accident Analysis & Prevention, 2015(74): 33-41. http://smartsearch.nstl.gov.cn/paper_detail.html?id=cbaa0e78017807ec30b13578bd8263eb
[8]	FRABONI F, MARÍN PUCHADES V, DE ANGELIS M, et al. Red-light running behavior of cyclists in Italy: An observational study[J]. Accident Analysis & Prevention, 2018(120): 219-232. http://www.ncbi.nlm.nih.gov/pubmed/30172107
[9]	SCHLEINITZ K, PETZOLDT T, KRÖLING S, et al. (E-)Cyclists running the red light-the influence of bicycle type and in-frastructure characteristics on red light violations[J]. Accident Analysis & Prevention, 2019(122): 99-107. http://www.ncbi.nlm.nih.gov/pubmed/30340148
[10]	ZHANG Yiqi, WU Changxu. The effects of sunshields on red light running behavior of cyclists and electric bike riders[J]. Accident Analysis & Prevention, 2013(52): 210-218. http://europepmc.org/abstract/med/23348101
[11]	TANG Tianpei, WANG Hua, MA Jie, et al. Analysis of crossing behavior and violations of electric bikers at signalized intersections[J/OL]. (2020-01)[2020-08-10]. https://www.hindawi.com/journals/jat/2020/3594963/.
[12]	YAO Lin, WU Changxu. Traffic safety for electric bike riders in China attitudes, risk perception, and aberrant riding behaviors[J]. Transportation Research Record, 2012, 2314(1): 49-56. doi: 10.3141/2314-07
[13]	YANG Hongtai, LIU Xiaohan, SU Fan, et al. Predicting e-bike users'intention to run the red light: An application and extension of the theory of planned behavior[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018(58): 282-291. http://www.sciencedirect.com/science/article/pii/S1369847817306976
[14]	GRAY J A. The Psychophysiological basis of introversion-extraversion[J]. Behaviour Research and Therapy, 1970, 8(3): 249-266. doi: 10.1016/0005-7967(70)90069-0
[15]	CONSTANTINOU E, PANAYIOTOU G, KONSTANTINOU N, et al. Risky and aggressive driving in young adults: Personality matters[J]. Accident Analysis & Prevention, 2011, 43(4): 1323-1331. http://www.ncbi.nlm.nih.gov/pubmed/21545861
[16]	CASTELLA J, PEREZ J. Sensitivity to punishment and sensitivity to reward and traffic violations[J]. Accident Analysis & Prevention, 2004, 36(6): 947-952. http://europepmc.org/abstract/MED/15350871
[17]	SCOTT-PARKER B, WATSON B, KING M J, et al. The influence of sensitivity to reward and punishment, propensity for sensation seeking, depression, and anxiety on the risky behaviour of novice drivers: A path model[J]. British Journal of Psychology, 2012, 103(2): 248-267. doi: 10.1111/j.2044-8295.2011.02069.x
[18]	GRAY J, MCNAUGHTON N. The neuropsychology of anxiety an enquiry into the functions of the septo-hippocampal system[M]. 2^nd Ed. Oxford: Oxford University Press, 2000.
[19]	CORR P J, COOPER A J. The reinforcement sensitivity theory of personality questionnaire(RST-PQ): Development and validation[J]. Psychological Assessment, 2016, 28(11): 1427-1440. doi: 10.1037/pas0000273
[20]	HARBECK E L, GLENDON A I. How reinforcement sensitivity and perceived risk influence young drivers'reported engagement in risky driving behaviors[J]. Accident Analysis & Prevention, 2013(54): 73-80.
[21]	AJZEN I. The theory of planned behavior[J]. Organizational Behavior and Human Decision Processes, 1991, 50(2): 179-211. doi: 10.1016/0749-5978(91)90020-T
[22]	SCOTT-PARKER B, WATSON B, KING M J, et al. Confirmatory factor analysis of the behaviour of young novice drivers scale(BYNDS)[J]. Accident Analysis & Prevention, 2012(49): 385-391. http://www.ncbi.nlm.nih.gov/pubmed/23036417
[23]	HARBECK E L, GLENDON A I, HINE T J. Reward versus punishment: Reinforcement sensitivity theory, young novice drivers'perceived risk, and risky Driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2017(47): 13-22. http://www.sciencedirect.com/science/article/pii/S136984781630170X
[24]	D'ARCY J, HERATH T. A review and analysis of deterrence theory in the is security literature: Making sense of the disparate findings[J]. European Journal of Information Systems, 2011, 20(6): 643-658. doi: 10.1057/ejis.2011.23
[25]	MURPHY K, HELMER I. Testing the importance of forgiveness for reducing repeat offending[J]. Australian and New Zealand Journal of Criminology, 2013(46): 138-156.
[26]	FLOYD D L, PRENTICE-DUNN S, ROGERS R W. A meta-analysis of research on protection motivation theory[J]. Journal of Applied Social Psychology, 2000(30): 407-429. doi: 10.1111/j.1559-1816.2000.tb02323.x
[27]	HOLLAND C, HILL R. The effect of age, gender and driver status on pedestrians'intentions to cross the road in risky situations[J]. Accident Analysis & Prevention, 2007, 39(2): 224-237. http://europepmc.org/abstract/MED/16979132
[28]	ZHOU Ronggang, HORREY W J, YU Ruifeng. The effect of conformity tendency on pedestrians'road-crossing intentions in China: An application of the theory of planned behavior[J]. Accident Analysis & Prevention, 2009, 41(3): 491-497. http://her.oxfordjournals.org/external-ref?access_num=19393798&link_type=MED
[29]	CHORLTON K, CONNER M, JAMSON S. Identifying the psychological determinants of risky riding: An application of an extended theory of planned behaviour[J]. Accident Analysis & Prevention, 2012(49): 142-153. http://www.ncbi.nlm.nih.gov/pubmed/23036391
[30]	MATSUI Y, OIKAWA S. Situational characteristics of fatal pedestrian accidents involving vehicles traveling at low speeds in Japan[J]. Traffic Injury Prevention, 2019(20): 1-6. doi: 10.1080/15389588.2019.1587166?src=recsys&
[31]	CASTANIER C, DEROCHE T, WOODMAN T. Theory of planned behaviour and road violations: The moderating influence of perceived behavioural control[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013(18): 148-158. http://www.sciencedirect.com/science/article/pii/S136984781300017X
[32]	NEMME H E, WHITE K M. Texting while driving: psychosocial influences on young people's texting intentions and behaviour[J]. Accident Analysis & Prevention, 2010, 42(4): 1257-1265.