Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach
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 diffi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6685004 |
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| author | Eui-Jin Kim |
| author_facet | Eui-Jin Kim |
| author_sort | Eui-Jin Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8b1e38be5e9c4d1e9e764feca4fdef47 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8b1e38be5e9c4d1e9e764feca4fdef472025-08-20T03:37:34ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66850046685004Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning ApproachEui-Jin Kim0Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Republic of KoreaUnderstanding 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.http://dx.doi.org/10.1155/2021/6685004 |
| spellingShingle | Eui-Jin Kim Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach Journal of Advanced Transportation |
| title | Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach |
| title_full | Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach |
| title_fullStr | Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach |
| title_full_unstemmed | Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach |
| title_short | Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach |
| title_sort | analysis of travel mode choice in seoul using an interpretable machine learning approach |
| url | http://dx.doi.org/10.1155/2021/6685004 |
| work_keys_str_mv | AT euijinkim analysisoftravelmodechoiceinseoulusinganinterpretablemachinelearningapproach |