Analysis and Prediction Model of Resident Travel Satisfaction

Zhenzhen Xu, Chunfu Shao, Shengyou Wang and Chunjiao Dong
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Zhenzhen Xu: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Chunfu Shao: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Shengyou Wang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Chunjiao Dong: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2020, vol. 12, issue 18, 1-15

Abstract: To promote the sustainable development of urban traffic and improve resident travel satisfaction, the significant factors affecting resident travel satisfaction are analyzed in this paper. An evaluation and prediction model for travel satisfaction based on support vector machine (SVM) is constructed. First, a multinomial logit (MNL) model is constructed to reveal the impact of individual attributes, family attributes and safety hazards on resident travel satisfaction and to clarify the significant factors. Then, a travel satisfaction evaluation model based on the SVM is constructed by taking significant factors as independent variables. Finally, travel optimization measures are proposed and the SVM model is used to predict the effect. Futian Street in Futian District of Shenzhen is taken as the object to carry out specific research. The results show that the following factors have a significant effect on resident travel satisfaction: age, job, level of education, number of car, income, residential area and potential safety hazards of people, vehicles, roads, environment, etc. The model fitting accuracy is 87.76%. The implementation of travel optimization measures may increase travel satisfaction rate by 14.07%.

Keywords: resident travel; satisfaction; support vector machine; travel optimization; policies and measures (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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