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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of advanced transportation 2020, Vol.2020 (2020), p.1-12
Main Authors: Afandizadeh Zargari, Shahriar, Safari, Farshid
Format: Article
Language:English
Subjects:
Cluster analysis
Clustering
Discretization
Econometrics
Logit models
Long term planning
Methods
Modelling
Researchers
Scheduling
Studies
Traffic assignment
Traffic congestion
Transportation planning
Travel
Travellers
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/7382569