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Exploring Behavioral Heterogeneities Of Elementary School Students’ Commute Mode Choices Through The Urban Travel Big Data

EXPLORING BEHAVIORAL HETEROGENEITIES OF ELEMENTARY SCHOOL STUDENTS’ COMMUTE MODE CHOICES THROUGH THE URBAN TRAVEL BIG DATA

  • Project Code :
  • TCREJA19_54
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EXPLORING BEHAVIORAL HETEROGENEITIES OF ELEMENTARY SCHOOL STUDENTS’ COMMUTE MODE CHOICES THROUGH THE URBAN TRAVEL BIG DATA

Exploring behavioral heterogeneities of elementary school students’ commute mode choices through the urban travel big data

Abstract:

Students’ commute mode choices have been recognized as an important factor affecting the physical and psychological health levels of children and urban traffic performance in peak hours. The influential patterns between most of the factors and students’ commute mode vary depending on the characteristics of the city. This study seeks to reveal such patterns specifically for elementary school students in Beijing, China, as those students’ commute behaviors have attracted appreciable attention from society. The data from the Beijing school Commute Survey conducted in December 2014 and January 2015 were adopted. To account for the unobserved heterogeneity, a finite mixture multinomial logit (FMMNL) model was developed. In comparing with the conventional multinomial logit (MNL) model, the FMMNL is superior due to the smaller AIC and BIC values. More importantly, the FMMNL model is versatile and ready to detect some complicated mode choice behaviors. For example, the results of the FMMNL model indicate that there are two types of students, those who tend to use a car, and those who tend to use a bicycle, as their grades increase. Such a heterogeneous pattern is difficult to be detected by conventional models. The finer results produced by the FMMNL model would be referenced for policymakers to design more targeted policies. Findings in this study could be referenced to other cities in China and the world.

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