Research on OD Matrix Calculation Based on Quantum Behaved Particle Swarm Optimization Algorithm
Lianyu WEI, Jianfu DU
DOI: 10.4236/jsea.2009.25045   PDF    HTML     5,432 Downloads   9,371 Views   Citations


Traffic information is so far less than the number of OD variables, that it is difficult to obtain the satisfactory solution. In this paper, a method based on Quantum behaved Particle Swarm Optimization (QPSO) algorithm is developed to obtain the global optimal solution. It designs the method based on QPSO algorithm to solve the OD matrix prediction model, lists the detailed steps and points out how to choose the PSO operator. Moreover, it uses MATLAB program-ming language to carry out the simulation test. The simulation results show that the method has higher efficiency and accuracy.

Share and Cite:

L. WEI and J. DU, "Research on OD Matrix Calculation Based on Quantum Behaved Particle Swarm Optimization Algorithm," Journal of Software Engineering and Applications, Vol. 2 No. 5, 2009, pp. 344-349. doi: 10.4236/jsea.2009.25045.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] W. Wang and J. Q. Xu, “Urban transportation planning theories and methods,” China Communications Press, 1992.
[2] S. N. Omkar, R. Khandelwal, T. V. S. Ananth, G. N. Naik, and S. Gopalakrishnan, “Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures,” Expert Systems with Applications, Vol. 36, No. 8, pp. 11312–11322, Oc-tober 2009.
[3] J. Kennedy and R. C. Eberhart, “Particle swarm optimiza-tion,” Proceedings IEEE International Conference Neu-ralNetworks [C]. Piscataway, NJ: IEEE Press, pp. 1942– 1948, 1995.
[4] C. Arndt, S. Robinson, and F. Tarp, “Parameter estimation for a computable general equilibrium model: a maximum entropy approach,” Economic Modelling, Vol. 19, No. 3, pp. 375–398, May 2002.
[5] Z. J. Gong, “Estimating the urban OD matrix: A neural network approach,” European Journal of Operational Re-search, Vol. 106, No. 1, pp. 108–115, April 1998.
[6] M. Brenninger-G?the, K. O. J?rnsten, and J. T. Lundgren, “Estimation of origin-destination matrices from traffic counts using multiobjective programming formulations,” Transportation Research Part B: Methodological, Vol. 23, No. 4, pp. 257–269, August 1989.
[7] G. R. Widom, “Data guides enabling query formulation and optimization in semistructured databases,” The 23rd VLDB[C], Athens, Greece, pp. 436–445, 1997.
[8] T. Milo and D. Suciu, “Index Structures for path expres-sions,” The 7th ICDT[C], pp. 277–295, 1999.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.