An Overview of PCATS/DEBNetS Micro-simulation System: Its Development, Extension, and Application to Demand Forecasting

The micro-simulator of individuals’ daily travel, PCATS, and a dynamic network simulator, DEBNetS, are integrated to form a simulation system for urban passenger travel. The components of the simulation system are briefly described, and three areas of on-going system improvement are described, i.e., (i) introduction of stochastic frontier models of prism vertex location, (ii) adoption of a fine grid system for quasi-continuous representation of space, and (iii) use of MCMC algorithms to handle colossal choice sets. Application case studies demonstrate that micro-simulation is a practical approach for demand forecasting and policy analysis, especially in the area of demand management.

[1]  David Damm,et al.  Parameters of activity behavior for use in travel analysis , 1982 .

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

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

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

[5]  Ryuichi Kitamura,et al.  A Mode and Destination Choice Model on a GIS Database: From Zone-Based toward Coordinates-Based Methodologies of Spatial Representation , 2000 .

[6]  Masatoshi Hatoko,et al.  Micm-Simulation Based Travel Demand Forecasting System for Urban Transportation Planning , 2000 .

[7]  Ryuichi Kitamura,et al.  A micro-simulation model system of individuals' daily activity behavior that incorporates spatial, temporal and coupling constraints , 1997 .

[8]  John W. Polak,et al.  STATIONARY STATES IN STOCHASTIC PROCESS MODELS OF TRAFFIC ASSIGNMENT: A MARKOV CHAIN MONTE CARLO APPROACH , 1996 .

[9]  R. Kitamura,et al.  Traveler Destination Choice Behavior: Effects of Time of Day, Activity Duration, and Home Location , 1998 .

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

[11]  Satoshi Fujii,et al.  Rule-based versus utility-maximizing models of activity-travel patterns , 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]  R. Kitamura,et al.  Comparative Analysis of Time-Space Prism Vertices for Out-of-Home Activity Engagement on Working and Nonworking Days , 2004 .

[14]  Satoshi Fujii,et al.  Improvement and Verification of Dynamic Traffic Flow Simulator “ DEBNetS” , 2002 .

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

[16]  Ryuichi Kitamura,et al.  Simulation of Destination Choice under Huge Choice Sets: Application of Markov Chain Monte Carlo Algorithms , 2001 .

[17]  Paul A. Ruud,et al.  Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results , 1996 .

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

[19]  Siddhartha Chib,et al.  Markov chain Monte Carlo and models of consideration set and parameter heterogeneity , 1998 .

[20]  Satoshi Fujii,et al.  A Household Attributes Generation System for Long-range Travel Demand Forecasting with Disaggregate Models , 2000 .