Comparison of Clustered-based MNL and Duration Models in Departure Time Choice Modelling

Abstract

One of the important decisions for travellers about each trip is the time that they depart from their point of origin. The aggregation of the departure times of all travellers forms the pattern of a temporal distribution of trips in a day and also peak periods of a day. In this paper, we applied the multinomial logit (MNL) on a choice set which was derived from cluster analysis, and we also made use of the duration models to estimate departure time for home-based work trips. For the duration models, the Kaplan-Meier and Cox proportional methods are used and then, the results are compared to cluster-based MNL model. This study is conducted in Mashhad city which has a population of about 2.7 million people. The results show the traveller's job and traveller's selected mode have a significant effect on his departure time choice. Comparison of the predictability power of these two modelling approaches indicates that the cluster-based MNL model in this case study is the preferable model.

Keywords

• clustered-based MNL
• cox proportional method
• Departure Time Choice (DTC)
• duration models
• K-mean clustering