Towards Knowledge-Oriented Smart Vehicle Adaptive Traffic Service

Article Preview

Abstract:

The most of very large traffic system by growing the variety of services, the relationships between the vehicle network and the infrastructure are more complex. Moreover, intelligent transportation systems are getting more and more to develop a better combination of travel safety and efficiency since long time ago. Vehicle is being evolved and traffic environment is especially also organized well-defined schedules priorities, which is real time based wireless network traffic condition, variable traffic condition, and traffic pattern from the vehicle navigation system. Accordingly, we propose to Knowledge-oriented Smart Vehicle Adaptive Traffic Service using genetic algorithm in this paper.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

459-462

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] A. Carter, The Status of Vehicle-to-Vehicle Communication as a Means of Improving Crash Prevention Performance, Tech. Rep. 05-0264, (2005).

Google Scholar

[2] V. Naumov and T. R. Gross, Connectivity-Aware Routing (CAR) in Vehicular Ad Hoc Networks, in INFOCOM. IEEE, May (2007).

DOI: 10.1109/infcom.2007.223

Google Scholar

[3] J. Zhao and G. Cao, VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks, IEEE Transactions on Vehicular Technology, vol. 57, no. 3, p.1910–1922, May (2008).

DOI: 10.1109/tvt.2007.901869

Google Scholar

[4] J. Jeong, S. Guo, Y. Gu, T. He, and D. Du, TBD: Trajectory-Based Data Forwarding for Light-Traffic Vehicular Networks, Tech. Rep. 08-040, (2008).

DOI: 10.1109/icdcs.2009.11

Google Scholar

[5] H. Wu, R. Fujimoto, R. Guensler, and M. Hunter, MDDV: A Mobility-Centric Data Dissemination Algorithm for Vehicular Networks, in VANET. ACM, Oct. (2004).

DOI: 10.1145/1023875.1023884

Google Scholar

[6] Michalewicz, Z. (1992) Genetic Algorithms + Data Structures = Evolutionary Programs, Springer-Verlag, AI Series, New York.

Google Scholar

[7] Spears, W. and DeJong, K. (1991) An Analysis of Multi-Point Crossover. Foundations of Genetic Algorithms, G. Rawlins, ed. Morgan-Kaufmann.

Google Scholar

[8] Starkweather, T., Whitley, D., and Mathias, K. (1991) Optimization Using Distributed Genetic Algorithms. Parallel Problem Solving from Nature, Springer Verlag.

DOI: 10.1007/bfb0029750

Google Scholar

[9] Jin-Hong Kim, Seung-Cheon Kim, Toward Hybrid Model for Architecture-oriented Sem-antic Schema of Self-adaptive System, International Conference on Green and Human Infor-mation Technology (ICGHIT 2013), LNCS, Springer-Verlag. Feb. (2013).

Google Scholar

[10] L.D.B. Navarro, M. Sdholt, R. Douence, and J. M. Menaud. Invasive patterns for distributed applications". In Proc. of the 9th International Symposium on Distributed Objects, Middleware, and Applications (DOA, 07), LNCS. Springer Verlag. Nov. (2007).

Google Scholar