Travel Mode Choice Analysis Using Support Vector Machines

In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.

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Mediation Effects of Income on Travel Mode Choice: Analysis of Short-Distance Trips Based on Path Analysis with Multiple Discrete Outcomes

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2664-03 ◽

2017 ◽

Vol 2664 (1) ◽

pp. 23-30 ◽

Cited By ~ 1

Author(s):

Hong T. A. Nguyen ◽

Makoto Chikaraishi ◽

Akimasa Fujiwara ◽

Junyi Zhang

Keyword(s):

Path Analysis ◽

Short Distance ◽

Mode Choice ◽

Travel Mode ◽

Mediation Effects ◽

Choice Analysis ◽

Travel Mode Choice

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211000750 ◽

2021 ◽

pp. 036119812110007

Author(s):

Kyusik Kim ◽

Kyusang Kwon ◽

Mark W. Horner

Keyword(s):

Random Forest ◽

Built Environment ◽

Mode Choice ◽

Age Groups ◽

Travel Mode ◽

Linear Relationships ◽

Heterogeneous Effects ◽

Choice Analysis ◽

Travel Mode Choice ◽

Selection For

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.

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Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model

Applied Sciences ◽

10.3390/app112411916 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11916

Author(s):

Yufeng Qian ◽

Mahdi Aghaabbasi ◽

Mujahid Ali ◽

Muwaffaq Alqurashi ◽

Bashir Salah ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Model ◽

Mode Choice ◽

Support Vector ◽

Choice Data ◽

Transport Planning ◽

Machine Model ◽

Minority Class ◽

Travel Mode ◽

Travel Mode Choice

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function’s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey–California dataset. The performance of the SVMAK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution.

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