Advertisement

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

  • Ryuichi Kitamura
  • Akira Kikuchi
  • Satoshi Fujii
  • Toshiyuki Yamamoto
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 31)

Abstract

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.

Keywords

Public Transit Markov Chain Monte Carlo Algorithm Travel Mode Terminal Vertex Stochastic Frontier Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arentze, T., A. Borgers, F. Hofman, S. Fujii, C. Joh, A. Kikuchi, R. Kitamura, H. Timmermans and P. van der Waerden (2001). Rule-based versus utility-maximizing models of activity-travel patterns: A comparison of empirical performance, In D. Hensher (ed.) Travel Behaviour Research: The Leading Edge, Elsevier Science, Oxford, pp.569–583.Google Scholar
  2. Beckman, R.J., K.A. Baggerly and M.D. McKay (1996). Creating synthetic baseline populations, Transportation Research A, 30A, 415–429.Google Scholar
  3. Chiang, J., S. Chib and C. Narasimhan (1999). Markov Chain Monte Carlo and models of consideration set and parameter heterogeneity, Journal of Econometrics, 89, 223–248.CrossRefGoogle Scholar
  4. Damm. D. (1982). Parameters of activity behavior for use in travel analysis, Transportation Research, 16A(2), 135–148.MathSciNetGoogle Scholar
  5. Hägerstrand, T. (1970). What about people in regional science?, Papers of the Regional Science Association, 24, 7–21.Google Scholar
  6. Hajivassiliou, V., D. McFadden and P. Ruud (1996). Simulation of multivariate normal rectangle probabilities and their derivatives: theoretical and computational results, Journal of Econometrics, 72, 85–134.MathSciNetCrossRefGoogle Scholar
  7. Hazelton, M.L., S. Lee and J.W. Polak (1996). Stationary states in stochastic process models of traffic assignment: a Markov Chain Monte Carlo approach, In J.-B. Lesort (ed.) Proceedings of the 13thInternational Symposium on Transportation and Traffic Theory, Pergamon, Oxford, pp. 341–357.Google Scholar
  8. Fujii, S., A. Kikuchi and R. Kitamura (2000). A micro-simulation analysis of the effects of transportation control measures to reduce CO2 emissions: a case study in Kyoto City, Traffic Engineering, 35(4), 11–18 (in Japanese).Google Scholar
  9. Fujii, S., M. Okushima, A. Kikuchi and R. Kitamura (1998). Development of a network flow simulator and evaluation of travel time, In the Proceedings of the 17thAnnual Meeting of the Japanese Society of Traffic Engineers, pp. 694–695 (in Japanese).Google Scholar
  10. Fujii, S., Y. Otsuka, R. Kitamura and T. Monma (1997). A micro-simulation model system of individuals’ daily activity behavior that incorporates spatial, temporal and coupling constraints, Infrastructure Planning Review, 14, 643–652 (in Japanese).Google Scholar
  11. Iida, Y., M. Iwabe, A. Kikuchi, R. Kitamura, K. Sakai, Y. Shiromizu, D. Nakagawa, M. Hatoko, S. Fujii, T. Morikawa and T. Yamamoto (2000). Micro-simulation based travel demand forecasting system for urban transportation planning, Infrastructure Planning Review, 17, 841–847 (in Japanese).Google Scholar
  12. Kawata, H., Y. Iida and Y. Shiromizu (1999). Case study of evaluation for comprehensive transportation policy, The Proceedings of the Infrastructure Planning Review Annual Meeting, 22(1), 511–514 (in Japanese).Google Scholar
  13. Kikuchi, A., S. Fujii and R. Kitamura (2001). Evaluation of transportation policies by micro-simulation of individuals’ behaviors on a coordinates system, City Planning Review, 36, 577–582 (in Japanese).Google Scholar
  14. Kikuchi, A., S. Fujii, Y. Shiromizu and R. Kitamura (2000). Calibration of DEBNetS on a large-scale network, In the proceedings of the 20thAnnual Meeting of the Japanese Society of Traffic Engineers, Tokyo, pp. 49–52 (in Japanese).Google Scholar
  15. Kikuchi, A, Y. Kato, T. Macuchi, S. Fujii and R. Kitamura (2002). Improvement and verification of dynamic traffic flow simulator “DEBNetS”, Infrastructure Planning Review, 19 (in press, in Japanese).Google Scholar
  16. Kikuchi, A., A. Kobata, S. Fujii and R. Kitamura (2000). A mode and destination choice model on a GIS database: from zone-based toward coordinates-based methodologies of spatial representation, Infrastructure Planning Review, 17, 841–847 (in Japanese).Google Scholar
  17. Kikuchi, A., T. Yamamoto, K. Ashikawa and R. Kitamura (2001). Computation of destination choice probabilities under huge choice sets: application of Markov Chain Monte Carlo algorithms, Infrastructure Planning Review, 18(4), 503–508 (in Japanese).Google Scholar
  18. Kikuchi, A., R. Kitamura, S. Kurauchi, K. Sasaki, T. Hanai, T. Morikawa, S. Fujii and T. Yamamoto (1999). Effect Analysis of Transportation Policies using Micro-Simulation Method-A Case Study of Toyota City-, The Proceedings of the Infrastructure Planning Review Annual Meeting, 22(1), 817–820 (in Japanese).Google Scholar
  19. Kitamura, R., C. Chen and R. Narayanan (1998). The effects of time of day, activity duration and home location on travelers’ destination choice behavior, Transportation Research Record, 1645, 76–81.Google Scholar
  20. Kitamura, R. and S. Fujii (1998). Two computational process models of activity-travel behavior, In T. Gärling, T. Laitila and K. Westin (eds.) Theoretical Foundations of Travel Choice Modelling, Pergamon Press, Oxford, pp. 251–279.Google Scholar
  21. Kitamura, R., S. Fujii, A. Kikuchi and T. Yamamoto (1998). Can TDM make urban transportation “sustainable”?: A micro-simulation study, Paper presented at International Symposium on Travel Demand Management, Newcastle, UK.Google Scholar
  22. Kitamura, R., S. Fujii, T. Yamamoto and A. Kikuchi (2000). Application of PCATS/DEBNetS to regional planning and policy analysis: Micro-simulation studies for the Cities of Osaka and Kyoto, Japan, In the Proceedings of Seminar F, European Transport Conference 2000, pp. 199–210.Google Scholar
  23. Kitamura, R., T. Yamamoto, K. Kishizawa and R.M. Pendyala (2000). Stochastic frontier models of prism vertices, Transportation Research Record, 1718, 18–26.Google Scholar
  24. Kitamura, R., T. Yamamoto, K. Kishizawa and R.M. Pendyala (2001). Prism-based accessibility measures and activity engagement, Paper presented at the 80thAnnual Meeting of the Transportation Research Board, Washington, D.C., January.Google Scholar
  25. Nishida, S., T. Yamamoto, S. Fujii and R. Kitamura (2000). A household attributes generation system for long-range travel demand forecasting with disaggregate models, Infrastructure Planning Review, 17, 779–787 (in Japanese).Google Scholar
  26. Pendyala, R.M., T. Yamamoto and R. Kitamura (2002). On the formation of time-space prisms to model constraints on personal activity-travel engagement, Transportation, 29(1), 73–94.CrossRefGoogle Scholar
  27. Schmidt, P. and A. Witte (1989). Predicting criminal recidivism using split population survival time models, Journal of Econometrics, 40, 141–159.CrossRefGoogle Scholar
  28. Yamamoto, Y., R. Kitamura and R.M. Pendyala (2002). Comparative analysis of time-space prism vertices for out-of-home activity engagement on working days and non-working days, Submitted to Geographical Analysis.Google Scholar
  29. Yamamoto, T., R. Kitamura and K. Kishizawa (2001). Sampling alternatives from a colossal choice set: an application of the MCMC algorithm, Transportation Research Record, 1752, 53–61.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Ryuichi Kitamura
    • 1
    • 2
  • Akira Kikuchi
    • 1
  • Satoshi Fujii
    • 3
  • Toshiyuki Yamamoto
    • 4
  1. 1.Department of Urban ManagementKyoto UniversityKyotoJapan
  2. 2.Department of Civil and Environmental EngineeringUniversity of CaliforniaDavisUSA
  3. 3.Department of Civil EngineeringTokyo Institute of TechnologyJapan
  4. 4.Department of Geotechnical and Environmental EngineeringNagoya UniversityJapan

Personalised recommendations