Analysis and Prediction Model of Resident Travel Satisfaction

• Main Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Sustainability
• Subjects: resident travel; satisfaction; support vector machine; travel optimization; policies and measures
• Online Access: https://www.mdpi.com/2071-1050/12/18/7522

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%.