Kangli Chen
School of Electronic and Information Engineering, Tongji University, Shanghai, 200092, China
Zhipeng Li
School of Electronic and Information Engineering, Tongji University, Shanghai, 200092, China
ABSTRACT
The prediction and identification of traffic state is a basic and important subject in the field of Intelligent Traffic System (ITS). In the previous researches, traffic information is usually gathered through pre-deployed sensors as well as other infrastructures, in which ways large costs are needed. The existing ways for estimating the traffic state mostly show the drawbacks of large computation and hard implementations. In this study, we propose a new way for estimating the traffic flow phase based on VANET communications. Firstly, we collect traffic information through VANET communications to reduce the costs effectively. Then, we present a prediction method based on fuzzy logic and its membership functions. This method shows the advantages of easier implementations and less computation and it can adapt dynamically to take more factors into considerations if needed. The simulation results based on NS2 and MOVE show that our proposed method gains satisfactory accuracy which can be used for further research based on the real-time traffic state.
PDF References Citation
How to cite this article
Kangli Chen and Zhipeng Li, 2013. Prediction of Traffic State Based on Fuzzy Logic in Vanet. Information Technology Journal, 12: 4642-4646.
DOI: 10.3923/itj.2013.4642.4646
URL: https://scialert.net/abstract/?doi=itj.2013.4642.4646
DOI: 10.3923/itj.2013.4642.4646
URL: https://scialert.net/abstract/?doi=itj.2013.4642.4646
REFERENCES
- Liu, J. and W. Guan, 2004. A summary of traffic flow forecasting methods. J. Highway Transp. Res. Devel., 21: 82-85.
Direct Link - Vaqar, S.A. and O. Basir, 2009. Traffic pattern detection in a partially deployed vehicular ad hoc network of vehicles. IEEE Wireless Commun., 16: 40-46.
CrossRef - Weng, X.X., Y.A. Tan, G.L. Du and Q.M. Hong, 2006. Prediction and Identification of Urban Traffic Flow Based on Features. Proceedings of the 9th International Conference ICARCV '06 on Control, Automation, Robotics and Vision, December 5-8, 2006, Singapore, pp: 1-6.
CrossRef