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KSCE Journal of Civil Engineering

, Volume 11, Issue 4, pp 199–207 | Cite as

Time-dependent origin-destination estimation: Genetic algorithm-based optimization with updated assignment matrix

  • Byungkyu “Brian” Park
  • Kangyuan Zhu
Transportation Engineering

Abstract

An estimation of origin-destination (OD) demand matrix is one of key elements to ensure the success in traffic modeling analysis. Even with widely deployed traffic sensors and advanced computational technologies, an estimation of time-dependent OD matrices is still a key barrier for the implementation of dynamic traffic assignment as well as simulation-based traffic modeling analysis. This paper proposes an improvement of existing time-dependent OD estimation method by updating assignment matrix at each step of OD estimation process and quantifies benefits and costs of doing so. The results from a case study with Florian network showed that the estimated OD flows from the proposed GA-based method with updated assignment matrix reduced the sum of square errors by 40% when compared with the OD flows from the DynaMIT OD estimation method with fixed assignment matrix, one of the most commonly used OD estimation methods. However, the proposed method would require significantly higher computational time than the traditional DyanMIT OD estimation method.

Keywords

time-dependent origin-destination matrix genetic algorithm dynamic traffic assignment simulation optimization 

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References

  1. Antoniou, C., Ben-Akiva M., and Koutsopoulos H. K. (2004). “Incroporating automated vehicle identification data into origindestination estimation.”Transportation Research Record 1882, pp. 37–44.Google Scholar
  2. Asakura, Y., Hato, E., and Kashiwadani, M., (2000). “OD Matrices estimation model using AVI data and its application to the han-shin expressway network.”Transportation, Vol. 27, No. 4, pp. 419–438.CrossRefGoogle Scholar
  3. Ashok, K., and Ben-Akiva, M. (2000). “Alternative approaches for realtime estimation and prediction of time-dependent origin-destination flow.”Transportation Science, Vol. 34, No. 1, pp. 21–36.MATHCrossRefGoogle Scholar
  4. Cascetta, E. and Nguyen, S. A. (1988). “A unified framework for estimating or updating origin/destination matrices from traffic counts.”Transportation Research 22B, pp. 437–455.MathSciNetGoogle Scholar
  5. Cascetta, E. (1984). “Estimation of trip matrices from traffic counts and survey data: Generalized least squares estimator.”Transportation Research 18B, pp. 289–299.Google Scholar
  6. Cascetta, E., Inaudi, D., and Marquis, G. (1993). “Dynamic estimatiors of origin-destination matrices using traffic counts.”Transportation Science, Vol. 27, No. 4, pp. 363–373.MATHCrossRefGoogle Scholar
  7. Cheung, W. M., Wong, S. C., and Tong, C. O. (2006). “Estimation of a time-dependent origin-destination matrix for congested high way networks.”Journal of Advanced Transportation, Vol. 40, No. 1, pp. 95–117.CrossRefGoogle Scholar
  8. Christopher, H. R., Joines, J. A., and Kay, M. G. (1995).A Genetic Algorithm for Function Optimization: A MATLAB Implementation. North Carolina State University, NCSU-IE TR 95-09.Google Scholar
  9. Dixon, M. P. and Rilett, L. R. (2002). “Real-time OD estimation using automatic vehicle identification and traffic count data.”Computer-Aided Civil and Infrastructure Engineering, Vol. 17, No. 1, pp. 7–21.CrossRefGoogle Scholar
  10. Florian, M. and Chen, Y. (1994). “A coordinate descent method for the bi-level O-D matrix adjustment problem.”Preprints of the 7th IFAC/IFORS Symposium on Transportation Systems: Theory and Application of Advanced Technology. Tianjin, China, August 24–26. pp. 1029–1034.Google Scholar
  11. Kim, H., Beak, S., and Lim, Y. (2001). “Origin-destination matrices estimated with a genetic algorithm from link traffic counts.”Transportation Research, Record 1771, National Research Council, Washington, D.C., pp. 156–63.Google Scholar
  12. Michalewicz, Z. (1994).Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin.MATHGoogle Scholar
  13. MIT (2003).Development of a Deployable Real-Time Dynamic Traffic Assignment System Evaluation Report (Part A): Evaluation of Estimation and Prediction Capabilities. Massachusetts Institute of Technology, Intelligent Transportation Systems Program.Google Scholar
  14. Park, B., Pampati, D. M., and Balakrishna, R. (2006). “Architecture for on-line deployment of DynaMIT in hampton roads, VA.”Applications of Advanced Technology in Transportation, Proceedings of the Ninth International Conference, Edited by Wang, Smith, Uzarski and Wong, American Society of Civil Engineers, pp. 605–610.Google Scholar
  15. Spiess, A. (1987). “Maximum-likelihood model for estimating origindestination matrices.”Transportation Research 21B, pp. 395–412.Google Scholar
  16. Tavana, H. and Mahmassani, H. S. (2001). “Estimation of dynamic origin-destination flows from sensor data using bi-level optimization method.”Transportation Research Board Annual Meeting, Washington, D.C.Google Scholar
  17. Torgil, A. (1998).Estimation of Origin-Destination Matrices using Traffic Counts—A Literature Survey. International Institute for Applied System Analysis, Interim Report IR-98-021/May, 1998.Google Scholar
  18. Wong, S. C., Tong, C. O., Wong, K. I., Lam, W. H. K., Lo, H. K., Yang, H., and Lo, H. P. (2005). “Estimation of multiclass origin-destination matrices from traffic counts.”Journal of Urban Planning and Development, Vol. 131, No. 1, pp. 19–29.CrossRefGoogle Scholar
  19. Yang, H. (1995). “Heuristic algorithm for the bilevel O-D matrix estimation problem.”Transportation research 29 B, pp. 231–242.Google Scholar
  20. Yang, H., Sasaki, T., Iida, Y., and Asakura, Y. (1992). “Estimation of origin-destination matrices from link traffic counts on congested networks”Transportation Research 26 B, pp. 417–434.Google Scholar
  21. Yin, Y. (2000). “Genetics algorithm based approach for bilevel programming models.”Journal of Transportation Engineering, Vol. 126, No. 2, pp. 115–120.CrossRefGoogle Scholar
  22. Yun, I. and Park, B. (2005). “Estimation of dynamic origin destination matrix: A genetic algorithm approach”Proceedings of IEEE International Conference on Intelligent Transportation Systems, Vienna, Austria.Google Scholar
  23. Zhou, X. and Mahmassani, H. (2005). “Dynamic OD demand estimation using automatic vehicle identification data.”Presented at the 84th Annual Meeting of the Transportation Research Board, Washington, D.C.Google Scholar
  24. Zhou, X. and Mahmassani, H. (2005). “Dynamic origin-destination demand estimation using multi-day link traffic counts for planning applications.”Presented at the 84th Annual Meeting of the Transportation Research Board Washington, D.C.Google Scholar

Copyright information

© KSCE and Springer jointly 2007

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of VirginiaCharlottesville
  2. 2.Department of Systems and Information EngineeringUniversity of VirginiaCharlottesville

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