Predicting transport mode choice preferences in a university district with decision tree-based models
Modeling and prediction of mode choice are essential to support more sustainable and safer transportation decisions. There is plenty of literature in this decade showing that machine learning (ML) models have been effective predicting techniques, although not easily interpretable. When these techniq...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | City and Environment Interactions |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259025202300020X |
| _version_ | 1827591035680718848 |
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| author | Jenny Díaz-Ramírez Juan Alberto Estrada-García Juliana Figueroa-Sayago |
| author_facet | Jenny Díaz-Ramírez Juan Alberto Estrada-García Juliana Figueroa-Sayago |
| author_sort | Jenny Díaz-Ramírez |
| collection | DOAJ |
| description | Modeling and prediction of mode choice are essential to support more sustainable and safer transportation decisions. There is plenty of literature in this decade showing that machine learning (ML) models have been effective predicting techniques, although not easily interpretable. When these techniques are used, there is a lack of connection with the data-gathering step, which is crucial to the technique selection and appropriate analysis of results. Based on a systematic literature review on mode choice studies, we present a methodology that interconnects the data-gathering process as a fundamental part of the descriptive phase when ML classification methods are used to predict mode choice preferences. The case study presented occurs in a university context whose descriptive phase shows interesting behavior patterns and highly imbalanced data in terms of mode choice. We show how decision tree methods allow us to tackle this issue in a contextualized manner and permit sensitivity analysis to test policies promoting changes in the modal split that aim for more sustainable mobility for the community of the university. |
| first_indexed | 2024-03-09T01:26:07Z |
| format | Article |
| id | doaj.art-236a426e471a46fcb20a124fa499b4ee |
| institution | Directory Open Access Journal |
| issn | 2590-2520 |
| language | English |
| last_indexed | 2024-03-09T01:26:07Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | City and Environment Interactions |
| spelling | doaj.art-236a426e471a46fcb20a124fa499b4ee2023-12-10T06:17:30ZengElsevierCity and Environment Interactions2590-25202023-12-0120100118Predicting transport mode choice preferences in a university district with decision tree-based modelsJenny Díaz-Ramírez0Juan Alberto Estrada-García1Juliana Figueroa-Sayago2Tecnologico de Monterrey, Mexico; Corresponding author at: School of Engineering and Science, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur Col. Tecnológico, C.P. 64849 Monterrey, Nuevo León, Mexico.Tecnologico de Monterrey, MexicoUniversidad de Monterrey, MexicoModeling and prediction of mode choice are essential to support more sustainable and safer transportation decisions. There is plenty of literature in this decade showing that machine learning (ML) models have been effective predicting techniques, although not easily interpretable. When these techniques are used, there is a lack of connection with the data-gathering step, which is crucial to the technique selection and appropriate analysis of results. Based on a systematic literature review on mode choice studies, we present a methodology that interconnects the data-gathering process as a fundamental part of the descriptive phase when ML classification methods are used to predict mode choice preferences. The case study presented occurs in a university context whose descriptive phase shows interesting behavior patterns and highly imbalanced data in terms of mode choice. We show how decision tree methods allow us to tackle this issue in a contextualized manner and permit sensitivity analysis to test policies promoting changes in the modal split that aim for more sustainable mobility for the community of the university.http://www.sciencedirect.com/science/article/pii/S259025202300020XMode choice modelsMachine learningDecision tree classifiersImbalanced data classification |
| spellingShingle | Jenny Díaz-Ramírez Juan Alberto Estrada-García Juliana Figueroa-Sayago Predicting transport mode choice preferences in a university district with decision tree-based models City and Environment Interactions Mode choice models Machine learning Decision tree classifiers Imbalanced data classification |
| title | Predicting transport mode choice preferences in a university district with decision tree-based models |
| title_full | Predicting transport mode choice preferences in a university district with decision tree-based models |
| title_fullStr | Predicting transport mode choice preferences in a university district with decision tree-based models |
| title_full_unstemmed | Predicting transport mode choice preferences in a university district with decision tree-based models |
| title_short | Predicting transport mode choice preferences in a university district with decision tree-based models |
| title_sort | predicting transport mode choice preferences in a university district with decision tree based models |
| topic | Mode choice models Machine learning Decision tree classifiers Imbalanced data classification |
| url | http://www.sciencedirect.com/science/article/pii/S259025202300020X |
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