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Applying a random forest method approach to model travel mode choice behavior

Long Cheng (UGent) , Xuewu Chen, Jonas De Vos (UGent) , Xinjun Lai and Frank Witlox (UGent)
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Abstract
The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people's travel behavior.
Keywords
BUILT ENVIRONMENT, RECOGNITION, UNCERTAINTY, ATTITUDES, SELECTION, WORK, Travel mode choice, Prediction performance, Variable importance, Random, forest, Nanjing (China)

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Chicago
Cheng, Long, Xuewu Chen, Jonas De Vos, Xinjun Lai, and Frank Witlox. 2019. “Applying a Random Forest Method Approach to Model Travel Mode Choice Behavior.” Travel Behaviour and Society 14: 1–10.
APA
Cheng, Long, Chen, X., De Vos, J., Lai, X., & Witlox, F. (2019). Applying a random forest method approach to model travel mode choice behavior. TRAVEL BEHAVIOUR AND SOCIETY, 14, 1–10.
Vancouver
1.
Cheng L, Chen X, De Vos J, Lai X, Witlox F. Applying a random forest method approach to model travel mode choice behavior. TRAVEL BEHAVIOUR AND SOCIETY. 2019;14:1–10.
MLA
Cheng, Long et al. “Applying a Random Forest Method Approach to Model Travel Mode Choice Behavior.” TRAVEL BEHAVIOUR AND SOCIETY 14 (2019): 1–10. Print.
@article{8619265,
  abstract     = {The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people's travel behavior.},
  author       = {Cheng, Long and Chen, Xuewu and De Vos, Jonas and Lai, Xinjun and Witlox, Frank},
  issn         = {2214-367X},
  journal      = {TRAVEL BEHAVIOUR AND SOCIETY},
  keywords     = {BUILT ENVIRONMENT,RECOGNITION,UNCERTAINTY,ATTITUDES,SELECTION,WORK,Travel mode choice,Prediction performance,Variable importance,Random,forest,Nanjing (China)},
  language     = {eng},
  pages        = {1--10},
  title        = {Applying a random forest method approach to model travel mode choice behavior},
  url          = {http://dx.doi.org/10.1016/j.tbs.2018.09.002},
  volume       = {14},
  year         = {2019},
}

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