Daily Commute Time Prediction Based on Genetic Algorithm

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.

[1]  Xiaoning Zhang,et al.  Integrated daily commuting patterns and optimal road tolls and parking fees in a linear city , 2008 .

[2]  K. Ozbay,et al.  Valuation of travel time and departure time choice in the presence of time-of-day pricing , 2008 .

[3]  Khandker Nurul Habib,et al.  Modeling commuting mode choice jointly with work start time and work duration , 2012 .

[4]  Satoshi Fujii,et al.  EVALUATION OF TRIP-INDUCING EFFECTS OF NEW FREEWAYS USING A STRUCTURAL EQUATIONS MODEL SYSTEM OF COMMUTERS' TIME USE AND TRAVEL , 2000 .

[5]  Khandker Nurul Habib,et al.  An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model , 2009 .

[6]  C. Bhat Analysis of travel mode and departure time choice for urban shopping trips , 1998 .

[7]  Zhicai Juan,et al.  Daily Travel Time Analysis with Duration Model , 2010 .

[8]  Chandra R. Bhat,et al.  A continuous-time model of departure time choice for urban shopping trips , 2002 .

[9]  Khandker Nurul Habib,et al.  Social Context of Activity Scheduling , 2008 .

[10]  D. Ettema,et al.  Modelling the joint choice of activity timing and duration , 2007 .

[11]  Ping-Feng Pai,et al.  System reliability forecasting by support vector machines with genetic algorithms , 2006, Math. Comput. Model..

[12]  Eric Cornelis,et al.  Travel and activity time allocation: An empirical comparison between eight cities in Europe , 2011 .

[13]  C. Bhat,et al.  An Exploration of the Relationship between Timing and Duration of Maintenance Activities , 2004 .

[14]  Peter Vovsha,et al.  Hybrid Discrete Choice Departure-Time and Duration Model for Scheduling Travel Tours , 2004 .

[15]  Chandra R. Bhat,et al.  A comprehensive daily activity-travel generation model system for workers , 2000 .

[16]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[17]  Nafis Ahmad,et al.  OPTIMIZATION OF PROCESS PLANNING PARAMETERS FOR ROTATIONAL COMPONENTS BY GENETIC ALGORITHMS , 2001 .

[18]  Fred Mannering,et al.  Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology , 1993, Transp. Sci..

[19]  Genevieve Giuliano,et al.  STAGGERED WORK HOURS FOR TRAFFIC MANAGEMENT: A CASE STUDY , 1990 .

[20]  M. Dijst,et al.  Travel-time ratios for visits to the workplace: the relationship between commuting time and work duration , 2002 .

[21]  B. Z. Yao,et al.  HYBRID MODEL FOR DISPLACEMENT PREDICTION OF TUNNEL SURROUNDING ROCK , 2012 .

[22]  Zhicai Juan,et al.  Examination of staggered shifts impacts on travel behavior: a case study of Beijing, China , 2013 .

[23]  Chandra R. Bhat,et al.  A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute , 1996 .

[24]  Davy Janssens,et al.  Allocating time and location information to activity-travel patterns through reinforcement learning , 2007, Knowl. Based Syst..

[25]  Chandra R. Bhat,et al.  A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity , 1996 .

[26]  Satoshi Fujii,et al.  The effects of a compressed working week on commuters' daily activity patterns , 2005 .

[27]  Rudy Hung,et al.  Using compressed workweeks to reduce work commuting , 1996 .

[28]  Kenneth A. Small,et al.  THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS , 1982 .

[29]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[30]  K. Small A Discrete Choice Model for Ordered Alternatives , 1987 .