Examining the Effects of the Built Environment on Travel Mode Choice across Different Age Groups in Seoul using a Random Forest Method

Author(s):  
Kyusik Kim ◽  
Kyusang Kwon ◽  
Mark W. Horner

It is important to analyze factors that influence travel mode choice and to predict individual mode choice because this shapes people’s movement and determines their level of mobility. While there have been studies investigating how built-environment elements are associated with travel mode choice, most efforts have neglected evaluating the heterogeneity of effects that the built environment has on travel mode choice across different age groups. This study aims to examine the effects of the built environment in influencing travel mode choice across age groups in Seoul, South Korea, using a random forest approach. Our random forest model demonstrates what factors are important and how they are associated with the effects on travel mode choice. As a result, the built environment has a greater impact on the subway selection for older adults than other age groups and the random forest approach captures non-linear relationships between certain predictors and travel mode choices. Applying this approach to the travel mode choice analysis, we can examine the heterogeneous effects of the built environment on travel mode choice across different age groups.

2019 ◽  
Vol 14 ◽  
pp. 1-10 ◽  
Author(s):  
Long Cheng ◽  
Xuewu Chen ◽  
Jonas De Vos ◽  
Xinjun Lai ◽  
Frank Witlox

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaolei Ma ◽  
Jie Yang ◽  
Chuan Ding ◽  
Jianfeng Liu ◽  
Quan Zhu

This paper aims to conduct an empirical study to evaluate the influence of built environment features and socioeconomic factors on commuters’ simultaneous choice of departure time and travel mode. Using Kunming, China, as the study region, the 2015 Regional Household Travel Survey and 2016 Point of Interest data are used in the analysis. The results show that, in addition to socioeconomic factors, built environment, such as the density of residential building, employment, and service facility are correlated with joint choice behavior. Moreover, there exist differences regarding the influence of built environment and socioeconomic factors on departure time and travel mode choice. The dissimilarity parameters show that commuters prefer to shift travel mode than departure time generally when travel condition alters. In order to examine the policy measures’ potential performance, the paper conducts simulation tests based on the Monte Carlo method. The simulation results show that congestion pricing of car travel during peak hours can reduce the number of commuting trips, and reducing travel time of public transit would be a better strategy to attract more passengers during peak hours. Moreover, reasonable land use planning, such as building more bus stops around commuters’ home location, would be a long term and fundamental approach to reduce mobile-source emissions and attract more public transit passengers.


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