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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 321574, 20 pages
http://dx.doi.org/10.1155/2012/321574
Research Article

Daily Commute Time Prediction Based on Genetic Algorithm

1College of Transportation, Jilin University, RenMin Street 5988, Changchun 130022, China
2Department of Civil Engineering, City College of New York, 160 Convent Avenue, New York, NY 10031, USA
3Transportation College, Dalian Maritime University, Dalian 116026, China
4College of Computer Science, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou 310023, China

Received 20 September 2012; Accepted 30 October 2012

Academic Editor: Baozhen Yao

Copyright © 2012 Fang Zong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a joint discrete-continuous model for activity-travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm (GA) is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and travel times for the daily commute trips are predicted and the influence of sociodemographic variables on activity-travel timing decisions is analyzed. Then the whole time allocation for the typical daily commute activities and trips is derived. The results indicate that the discrete choice model and the continuous model match well in the calculation of activity-travel schedule. The results also show that the genetic algorithm contributes to the optimization and thus the high accuracy of the hazard model. The developed joint discrete-continuous model can be used to predict the agenda of a simple daily activity-travel pattern containing only work, and it provides potential for transportation demand management policy analysis.