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%.
Highlights
With the rapid development of economy and society, the number of motor vehicles is increasing yearly
Acheampong [22] used the Logistic regression model to analyze the influence of the type of land used by commuters in African cities on their attendance patterns. These models can be used in studies of correlations with significant interactions: Natalia Casado-Sanz et al [23] used a multinomial logit (MNL) model to find the most important factors involved in driver injury severity and the statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and infractions, increase the severity of the drivers’ injuries
In the resident travel satisfaction evaluation model, the characteristic attributes with significant influence are used as the test sample data xi to input the support vector machine (SVM) resident travel satisfaction evaluation model and yi is output as the results of resident travel satisfaction after the implementation of optimization measures
Summary
With the rapid development of economy and society, the number of motor vehicles is increasing yearly. The proportion of residents relying on cars for travel has been increasing, which is leading to increasingly serious congestion of urban roads and the frequent occurrence of various travel-safety problems. By analyzing the hidden safety hazards in “people, cars, roads and environment” and mining the significant factors affecting resident travel satisfaction, the travel environment can be optimized in a targeted way to ensure residents’ travel safety and improve their travel satisfaction. It can provide a certain reference for formulating traffic policies and allocating resources reasonably
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