Using Clustering Methods in Multinomial Logit Model for Departure Time Choice
Travellers have to make some decisions for each trip, and one of them is the choice of departure time. Discrete choice models have been employed as an approach to departure time modelling by many researchers. In this method, preparing choice set is a primary challenge which involves the definition o...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/7382569 |
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| author | Shahriar Afandizadeh Zargari Farshid Safari |
| author_facet | Shahriar Afandizadeh Zargari Farshid Safari |
| author_sort | Shahriar Afandizadeh Zargari |
| collection | DOAJ |
| description | Travellers have to make some decisions for each trip, and one of them is the choice of departure time. Discrete choice models have been employed as an approach to departure time modelling by many researchers. In this method, preparing choice set is a primary challenge which involves the definition of some departure periods to be selected by the traveller. In this research, choice sets were formed by applying the clustering methods on departure times. Afterwards, we developed Multinomial Logit (MNL) models on different choice sets and compared the models. The data used throughout this research belonged to Mashhad City. Research results indicated that Ward’s hierarchical clustering method is improper for time discretization; furthermore, the K-means clustering method is more efficient than the expectation maximization and K-medoids methods in the time discretization for MNL modelling. The developed model (based on K-means clustering method) accurately predicts departure time for 58% of persons within the test group, which reflects the effectiveness of the resulting model compared to the 36% which is obtained without the model. |
| format | Article |
| id | doaj-art-a3cd31cdf9694d29a730d5c32928d82f |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a3cd31cdf9694d29a730d5c32928d82f2025-02-03T05:45:45ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/73825697382569Using Clustering Methods in Multinomial Logit Model for Departure Time ChoiceShahriar Afandizadeh Zargari0Farshid Safari1Department of Transportation Engineering and Planning, School of Civil Engineering, Iran University of Science and Technology, Tehran 16846-13114, IranDepartment of Transportation Engineering and Planning, School of Civil Engineering, Iran University of Science and Technology, Tehran 16846-13114, IranTravellers have to make some decisions for each trip, and one of them is the choice of departure time. Discrete choice models have been employed as an approach to departure time modelling by many researchers. In this method, preparing choice set is a primary challenge which involves the definition of some departure periods to be selected by the traveller. In this research, choice sets were formed by applying the clustering methods on departure times. Afterwards, we developed Multinomial Logit (MNL) models on different choice sets and compared the models. The data used throughout this research belonged to Mashhad City. Research results indicated that Ward’s hierarchical clustering method is improper for time discretization; furthermore, the K-means clustering method is more efficient than the expectation maximization and K-medoids methods in the time discretization for MNL modelling. The developed model (based on K-means clustering method) accurately predicts departure time for 58% of persons within the test group, which reflects the effectiveness of the resulting model compared to the 36% which is obtained without the model.http://dx.doi.org/10.1155/2020/7382569 |
| spellingShingle | Shahriar Afandizadeh Zargari Farshid Safari Using Clustering Methods in Multinomial Logit Model for Departure Time Choice Journal of Advanced Transportation |
| title | Using Clustering Methods in Multinomial Logit Model for Departure Time Choice |
| title_full | Using Clustering Methods in Multinomial Logit Model for Departure Time Choice |
| title_fullStr | Using Clustering Methods in Multinomial Logit Model for Departure Time Choice |
| title_full_unstemmed | Using Clustering Methods in Multinomial Logit Model for Departure Time Choice |
| title_short | Using Clustering Methods in Multinomial Logit Model for Departure Time Choice |
| title_sort | using clustering methods in multinomial logit model for departure time choice |
| url | http://dx.doi.org/10.1155/2020/7382569 |
| work_keys_str_mv | AT shahriarafandizadehzargari usingclusteringmethodsinmultinomiallogitmodelfordeparturetimechoice AT farshidsafari usingclusteringmethodsinmultinomiallogitmodelfordeparturetimechoice |