Same-Day Mode Choice Modeling with Household Vehicle Usage Simulation in Developing Countries

2011 ◽  
Vol 2239 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Xia Wan ◽  
Yi Zhang ◽  
Peter J. Jin ◽  
Bin Ran ◽  
Wei Wang ◽  
...  
Keyword(s):  
Developing Countries ◽  
Mode Choice ◽  
Choice Modeling ◽  
Vehicle Usage ◽  
Mode Choice Modeling

scholarly journals Current issues in mode choice modeling

2011 ◽  
Vol 38 (4) ◽  
pp. 581-585 ◽  
Author(s):  
Maya Abou-Zeid ◽  
Darren M. Scott
Keyword(s):  
Mode Choice ◽  
Choice Modeling ◽  
Mode Choice Modeling

Mode choice modeling for long-distance travel

2014 ◽  
Vol 7 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Rolf Moeckel ◽  
Rhett Fussell ◽  
Rick Donnelly
Keyword(s):  
Mode Choice ◽  
Choice Modeling ◽  
Long Distance ◽  
Distance Travel ◽  
Long Distance Travel ◽  
Mode Choice Modeling

scholarly journals Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Eui-Jin Kim
Keyword(s):  
Machine Learning ◽  
Choice Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Variable Importance ◽  
Choice Modeling ◽  
Travel Mode ◽  
Extreme Gradient Boosting ◽  
Travel Mode Choice ◽  
Mode Choice Modeling

Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.

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