Understanding the Impacts of Safety Perception on Changing Travel Patterns

A Comparative Analysis

Bibliographic Data

ID22006203
AuthorsHao Xu (0000-0002-6507-4436, Ankang University), Shengyu Zhang (0000-0001-7480-398X, Central China Normal University), Wei Liu (0000-0002-5949-0302, East China Jiaotong University, corresponding author), Junhua Guo (0000-0002-4729-163X, East China Jiaotong University)
Year2025
Volume15
Issue4
Publication date2025-10-01
Open AccessYes
TypeARTICLE
VenueSAGE Open (JOURNAL)
Journal identifiersISSN: 2158-2440 • E-ISSN: 2158-2440
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/21582440251391906
OpenAlexW4416130814
LanguageEN
References cited35

This article analyzes the factors influencing travel behavior during the epidemic and explore the impact of safety perception on travel choices. The study first summarizes the theoretical basis of factors affecting travel mode choice and introduces the corresponding questionnaire design. One thousand two hundred twenty-eight valid questionnaire data were collected in December 2020, including 1,044 online and 184 interview questionnaires. Descriptive statistics, correspondence analysis, and multivariate logistic regression are then used to systematically analyze the influencing factors on travel behavior before and after the epidemic. Additionally, a random parameter binary logit model with mean heterogeneity is applied to explore factors related to travel safety perceptions during the epidemic. The results reveal significant differences in influencing factors between self-driving/taxi and other modes of transportation, such as electric vehicles/bicycles, subways, and buses. Marital status (married, 1.131) and household size (household size: 3, 1.782), education (senior high, −1.650) contribute significantly to the composition of the random parameter binary logit model. Based on the findings, it is recommended that theoretically, future research could further explore the relationship between demographic characteristics and safety perception in travel choices; practically, policymakers should tailor transportation policies for different demographic groups, particularly focusing on safety perceptions

Binary logit model · Descriptive statistics · Logistic regression · Logit · Marital status · Ordered logit · Perception · Risk perception · Travel behavior · Traffic and Road Safety · Transportation and Mobility Innovations · Urban Transport and Accessibility

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Open DOIOpen Access