Searching for the Inflection Point of Travel Well-Being from the Views of Travel Characteristics Based on the Ordered Logistic Regression Model

Hongmei Yu, Xiaofei Ye (), Xingchen Yan, Tao Wang, Jun Chen and Bin Ran
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Hongmei Yu: Jiangsu Modern Urban Transportation Technology Collaborative Innovation Center, Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China
Xiaofei Ye: Jiangsu Modern Urban Transportation Technology Collaborative Innovation Center, Faculty of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China
Xingchen Yan: College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China
Tao Wang: School of Architecture and Transportation, Guilin University of Electronic Technology, Lingjinji Road 1#, Guilin 541004, China
Jun Chen: School of Transportation, Southeast University, Si Pai Lou 2#, Nanjing 210096, China
Bin Ran: Department of Civil and Environmental Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA

Sustainability, 2023, vol. 15, issue 21, 1-20

Abstract: Travel well-being is the subjective feeling of satisfaction that people have while traveling. Previous research focused on its determinants and relationships with subjective well-being ignored. But no quantitative study discusses the effect of characteristics like weekly income and travel time on travel well-being. To demonstrate the quantitative inflection of travel well-being from characteristics, the relevant factors influencing travel well-being as the dependent variable are identified using Pearson correlation analysis and linear regression in this paper. To overcome the limitations of linear regression techniques, ordered logistic regression is applied to establish an analytical model of travel well-being for predicting the response probabilities for different degrees based on combinations of explanatory variables. Both the linear regression and ordered logistic regression models are calibrated by American residents’ travel datasets. The results illustrate that the ordered logistic model fits sample data better than linear regression models. Age, travel time, health status, and resting degree are significantly related to travel well-being. Older people and those who are healthier and better rested are more likely to experience higher levels of travel well-being. Additionally, increased travel time is associated with a significant decrease in travel well-being. Therefore, to enhance people’s travel feelings, policymakers and urban planners can enhance the quality of public transportation services and provide diverse transportation options, while also logically constructing transportation hubs to provide more convenient travel plans.

Keywords: travel well-being; travel behavior; ordered logistic regression model; policy measures (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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