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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- 被引次数: 0
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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