社交网络交通信息对出行方式选择行为影响模型
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摘要
为解决社交网络交通信息对出行方式选择行为影响缺少定量描述方法的问题,基于技术接受理论,提出感知有用性、感知易用性、感知风险性等7个潜变量作为出行者对社交网络交通信息的心理感受变量.结合个人属性变量、出行方案属性变量共同构建融合社交网络交通信息影响的出行方式选择行为混合离散模型,运用重庆市问卷调查数据进行实证分析.结果表明,混合模型优度比提升了0.171,同时,心理感受变量中感知风险性对选择结果有负向影响,其余变量对选择结果均为正向影响,而感知有用性(0.757)、主观规范(0.646)、感知信任(0.502)对结果影响最大.
This study mainly deals with the problem of quantitatively measuring the influence of social network traffic information on travel mode choice behavior. According to the Technology Acceptance Model, 7 latent variables, including perceived usefulness, perceived usability, perceived risk etc., are proposed as the perceived variables of travelers on social network traffic information. A hybrid discrete travel mode choice model is constructed by combining personal attribute variables and travel plan variables with social network traffic information. The empirical analysis is conducted using the questionnaire of Chongqing city in China. The results from the hybrid model show an improvement of 0.171 in goodness of fit. The variable of perceived risk shows negative effect on the travel mode choice, while the other variables show positive effect. The three variables of perceived usefulness, subjective norm, and perceived trust show the most significant effect on the selection, of which the coefficients are 0.757, 0.646 and 0.502 respectively.
This study mainly deals with the problem of quantitatively measuring the influence of social network traffic information on travel mode choice behavior. According to the Technology Acceptance Model, 7 latent variables, including perceived usefulness, perceived usability, perceived risk etc., are proposed as the perceived variables of travelers on social network traffic information. A hybrid discrete travel mode choice model is constructed by combining personal attribute variables and travel plan variables with social network traffic information. The empirical analysis is conducted using the questionnaire of Chongqing city in China. The results from the hybrid model show an improvement of 0.171 in goodness of fit. The variable of perceived risk shows negative effect on the travel mode choice, while the other variables show positive effect. The three variables of perceived usefulness, subjective norm, and perceived trust show the most significant effect on the selection, of which the coefficients are 0.757, 0.646 and 0.502 respectively.
引文
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[7]赵阳,邵昀泓.基于活动的出行行为动态模拟方法[J].系统工程理论与实践, 2008, 28(9):159-165.[ZHAO Y, SHAO Y H. The activity-based dynamic simulation method of travel behavior[J]. Systems EngineeringTheory&Practice, 2008, 28(9):159-165.]
[8] SELTEN R, CHMURA T, PITZ T, et al. Commuters route choice behaviour[J]. Games and Economic Behavior, 2007, 58(2):394-406.
[9] TERRY E D, EYRAN J G, AMNON R. Departure times in Y-shaped traffic networks with multiple bottlenecks[J]. American Economic Review, 2009, 99(5):2149-2176.
[10]唐立,邹彤,罗霞,等.基于混合Logit模型的网约车选择行为研究[J].交通运输系统工程与信息, 2018, 18(1):108-114.[TANG L, ZOU T, LUO X, et al. Choice behavior of taxi-hailing based on Mixed-Logit model[J].Journal of Transportation Systems Engineering and Information Technology, 2018, 18(1):108-114.]
[11] DAVIS F D. Perceived usefulness, perceived ease of use,and user acceptance of information technology[J]. Mis Quarterly, 1989, 13(3):319-340.
[12] MUELLER R O. Structural equation modeling:Back to basics[J]. Structural Equation Modeling:A Multidisciplinary Journal, 1997, 4(4):353-369.
[2] ARAL S, WALKER D. Identifying influential and susceptible members of social networks[J]. Science,2012, 337(6092):337-341.
[3]张赛,徐恪,李海涛.微博类社交网络中信息传播的测量与分析[J].西安交通大学学报, 2013, 47(2):124-129.[ZHANG S, XU K, LI H T. Measurement and analysis of information propagation in online social networks like microblog[J]. Journal of Xi'an Jiaotong University, 2013, 47(2):124-129.]
[4] HACKNEY J, MARCHAL F. A coupled multi-agent microsimulation of social interactions and transportation behavior[J]. Transportation Research Part A:Policy and Practice, 2011, 45(4):296-309.
[5] BERG P V D, ARENTZE T, TIMMERMANS H. A path analysis of social networks, telecommunication and social activity-travel patterns[J]. Transportation Research Part C:Emerging Technologies, 2013, 26(1):256-268.
[6]刘天亮,张冲,王天歌,等.朋友圈信息交互对个体出行决策行为的影响研究[J].交通运输系统工程与信息, 2013, 13(6):86-93.[LIU T L, ZHANG C, WANG T G, et al. Effects of friends'information interaction on travel decisions[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(6):86-93.]
[7]赵阳,邵昀泓.基于活动的出行行为动态模拟方法[J].系统工程理论与实践, 2008, 28(9):159-165.[ZHAO Y, SHAO Y H. The activity-based dynamic simulation method of travel behavior[J]. Systems EngineeringTheory&Practice, 2008, 28(9):159-165.]
[8] SELTEN R, CHMURA T, PITZ T, et al. Commuters route choice behaviour[J]. Games and Economic Behavior, 2007, 58(2):394-406.
[9] TERRY E D, EYRAN J G, AMNON R. Departure times in Y-shaped traffic networks with multiple bottlenecks[J]. American Economic Review, 2009, 99(5):2149-2176.
[10]唐立,邹彤,罗霞,等.基于混合Logit模型的网约车选择行为研究[J].交通运输系统工程与信息, 2018, 18(1):108-114.[TANG L, ZOU T, LUO X, et al. Choice behavior of taxi-hailing based on Mixed-Logit model[J].Journal of Transportation Systems Engineering and Information Technology, 2018, 18(1):108-114.]
[11] DAVIS F D. Perceived usefulness, perceived ease of use,and user acceptance of information technology[J]. Mis Quarterly, 1989, 13(3):319-340.
[12] MUELLER R O. Structural equation modeling:Back to basics[J]. Structural Equation Modeling:A Multidisciplinary Journal, 1997, 4(4):353-369.