基于潜在类别的无人驾驶汽车选择行为

doi:10.13229/j.cnki.jdxbgxb20200390

Abstract

Abstract:

In order to analyze the preferences heterogeneity of travelers and the influence of autonomous vehicles on travel behavior, this study incorporates the latent psychological variables into the latent-class conditional Logit model based on the theoretical framework of the extended technology acceptance model. A hybrid choice model is established to conduct empirical research. The results show that compared with the traditional conditional Logit model, the latent-class conditional Logit model has higher fitness. Respondents can be divided into three subgroups: the shared autonomous vehicles preference subgroup, car preference subgroup, and private autonomous vehicles preference subgroup. The three subgroups account for 46.5%, 13.0% and 40.5%, respectively. Travelers from the car preference subgroup evaluate the walking and waiting time and travel costs positively, while travelers from the private autonomous vehicle preference subgroup evaluate parking costs negatively. The two latent variables of perceived ease of use and perceived trust have a significant influence on the classification of latent class.

Key words: traffic engineering, travel mode choice, latent-class conditional Logit model, latent variable, autonomous vehicle

CLC Number:

U121

Zhi-wei LIU,Jian-rong LIU,Wei DENG. Travelers′ choice behavior of autonomous vehicles based on latent class[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1261-1268.

Figures/Tables 9

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References 17

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潜在变量	显示变量	符号
感知有用（PU）	无人驾驶车辆可以降低交通事故	pu1
	无人驾驶车辆可以降低交通拥堵	pu 2
	无人驾驶车辆可以降低交通成本	pu 3
	无人驾驶车辆可以改善驾驶行为表现	pu 4
感知易用（PEU）	学习使用无人驾驶车辆对我来说很简单	peu 1
	无人驾驶车辆的使用很清晰和容易理解	peu 2
	无人驾驶车辆的使用不需要太多的心理负担	peu 3
感知信任（PT）	无人驾驶车辆是可靠的	pt1
	无人驾驶车辆是值得信赖的	pt 2
	总之，我完全相信无人驾驶车辆	pt 3
社会影响（SN）	对我很重要的人支持我使用无人驾驶车辆	sn1
	对我很重要的人希望我将来能使用无人驾驶车辆	sn 2
	如果身边的人使用无人驾驶车辆，我也会使用的	sn 3
行为意向（BIU）	未来我会使用无人驾驶车辆	biu1
	未来我会购买无人驾驶车辆	biu2
	我会向亲朋好友推荐使用无人驾驶车辆	biu3

变量	表示符号	定义	百分比/%
性别	GEND	女	45.73
性别	GEND	男	54.27
年龄	AGE1	30岁及以下	66.00
	AGE2	31~45岁	13.90
	AGE3	46~55岁	12.23
	AGE4	55岁以上	7.87
受教育程度	EDU	高中及以下	10.05
		大专	11.39
		本科	66.67
		硕士及以上	11.89
家庭月收入（元/月）	HINC1	5000以下	24.29
	HINC2	5000~10000	41.71
	HINC3	10000~20000	21.60
	HINC4	20000以上	12.40
是否有学龄儿童	CHILD	是	53.10
是否有学龄儿童	CHILD	否	46.90
是否拥有驾照	LICENSE	是	42.38
是否拥有驾照	LICENSE	否	57.62
家庭是否拥有小汽车	CAR	是	63.99
家庭是否拥有小汽车	CAR	否	36.01
是否拥有公交 IC卡	ICARD	是	77.72
是否拥有公交 IC卡	ICARD	否	22.28
家庭总人口数	HSIZE	1	5.36
		2	9.21
		3	40.20
		4	20.60
		5人及以上	24.62
通勤方式	CAR2WORK	使用小汽车上班	30.65
	BUS2WORK	使用公交车上班	18.09
	TRAIN2WORK	使用地铁上班	21.94
	OTHERS	其他	29.32

变量	题项	KMO	因子载荷	Cronbach′s Alpha
感知有用性（PU）	pu 1	0.8507	0.918	0.931
	pu 2		0.927
	pu 3		0.923
	pu 4		0.876
感知易用性（EU）	peu 1	0.7750	0.956	0.956
	peu 2		0.964
	peu 3		0.954
感知信任（PT）	pt 1	0.7680	0.974	0.965
	pt 2		0.972
	pt 3		0.955
社会影响（SN）	sn 1	0.7680	0.974	0.965
	sn 2		0.972
	sn 3		0.955
行为意向（BIU）	biu 1	0.7760	0.979	0.932
	biu 2		0.971
	biu 3		0.968

	RMSEA	CFI	TLI	SRMR
参考值	≤0.08	≥0.900	≥0.900	<0.08
模型参数	0.08	0.976	0.967	0.020

类别	CAIC	BIC
2	3682	3655
3	2731	2683
4	2784	2715
5	2848	2758
6	2928	2817

Travelers′ choice behavior of autonomous vehicles based on latent class

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