Florida activity mobility simulator - Overview and preliminary validation results

The development of modeling systems for activity-based travel demand ushers in a new era in transportation demand forecasting and planning. A comprehensive multimodal activity-based system for forecasting travel demand was developed for implementation in Florida and resulted in the Florida Activity Mobility Simulator (FAMOS). Two main modules compose the FAMOS microsimulation model system for modeling activity-travel patterns of individuals: the Household Attributes Generation System and the Prism-Constrained Activity-Travel Simulator. FAMOS was developed and estimated with household activity and travel data collected in southeast Florida in 2000. Results of the model development effort are promising and demonstrate the applicability of activity-based model systems in travel demand forecasting. An overview of the model system, a description of its features and capabilities, and preliminary validation results are provided.

[1]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[2]  P. Schmidt,et al.  Predicting Criminal Recidivism Using "Split Population" Survival Time Models , 1987 .

[3]  K. Axhausen,et al.  Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems , 1992 .

[4]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

[5]  Ryuichi Kitamura,et al.  Generation of Synthetic Daily Activity-Travel Patterns , 1997 .

[6]  Ryuichi Kitamura,et al.  AN ACTIVITY-BASED MICROSIMULATION ANALYSIS OF TRANSPORTATION CONTROL MEASURES , 1997 .

[7]  Ryuichi Kitamura,et al.  Application of an Activity-Based Travel-Demand Model Incorporating a Rule-Based Algorithm , 1998 .

[8]  R. Pendyala,et al.  Coast-to-Coast Comparison of Time Use and Activity Patterns , 2000 .

[9]  Satoshi Fujii,et al.  Rule-based versus utility-maximizing models of activity-travel patterns , 2000 .

[10]  R. Kitamura,et al.  Stochastic Frontier Models of Prism Vertices , 2000 .

[11]  Ryuichi Kitamura,et al.  Micro-simulation of daily activity-travel patterns for travel demand forecasting , 2000 .

[12]  S. Fujii,et al.  APPLICATION OF PCATS/DEBNETS TO REGIONAL PLANNING AND POLICY ANALYSIS: MICRO-SIMULATION STUDIES FOR THE CITIES OF OSAKA AND KYOTO, JAPAN , 2000 .

[13]  Toshiyuki Yamamoto,et al.  Sampling Alternatives from Colossal Choice Set: Application of Markov Chain Monte Carlo Algorithm , 2001 .

[14]  F. Koppelman,et al.  History Dependency in Daily Activity Participation and Time Allocation for Commuters , 2002 .

[15]  O. Johansson-Stenman,et al.  A Conceptual Analysis of the Impact of Travel Demand Management on Private Car Use , 2002 .

[16]  Ram M. Pendyala,et al.  A NESTED LOGIT MODEL OF COMMUTERS' ACTIVITY SCHEDULES , 2002 .

[17]  Toshiyuki Yamamoto,et al.  On the formulation of time-space prisms to model constraints on personal activity-travel engagement , 2002 .

[18]  R. Pendyala,et al.  Modeling Time and Task Allocation among Household Members for Simulating Household Activity Travel Patterns , 2002 .

[19]  Konstadinos G. Goulias,et al.  Transportation Systems Planning : Methods and Applications , 2002 .

[20]  Peter Vovsha,et al.  Explicit Modeling of Joint Travel by Household Members: Statistical Evidence and Applied Approach , 2003 .

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

[22]  Xin Ye,et al.  Departure-Time Choice and Mode Choice for Nonwork Trips: Alternative Formulations of Joint Model Systems , 2004 .

[23]  Chandra R. Bhat,et al.  Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns , 2004 .

[24]  R. Kitamura,et al.  Comparative Analysis of Time-Space Prism Vertices for Out-of-Home Activity Engagement on Working and Nonworking Days , 2004 .

[25]  Xin Ye,et al.  An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns , 2007 .