A Smart Hybrid System for Parking Space Reservation in VANET
محورهای موضوعی : Vehicular NetworksFarhad Rad 1 , hadi pazhokhzadeh 2 , hamid parvin 3
1 - yasooj branch,islamic azad university
2 - islamic azad university
3 - islamic azad university
کلید واژه: ANFIS, reservation, VANET, multi-objective genetic algorithm,
چکیده مقاله :
Nowadays, developed and developing countries using smart systems to solve their transportation problems. Parking guidance intelligent systems for finding an available parking space, are considered one of the architectural requirements in transportation. In this paper, we present a parking space reservation method based on adaptive neuro-fuzzy system(ANFIS) and multi-objective genetic algorithm. In modeling of this system, final destination, searching time and cost of parking space have been used. Also, we use the vehicle ad-hoc network (VANET) and time series, for traffic flow predict and choose the best path. The benefits of the proposed system are declining searching time, average the walking and travel time. Evaluations have been performed by the MATLAB and we can see that the proposed method makes a good sum of best cost which is useful and meaningful in a parking space reserved for drivers and facility managers. The simulation results show that the performance and accuracy of the method have been significantly improved compared to previous works.
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[2]H. Wang and W. He, A reservation-based smart parking system. in Computer Communications Workshops (INFOCOM WKSHPS), pp. 690-695. IEEE.
[3]H. Zhao, L. Lu, C. Song, and Y. Wu, 2012. IPARK: Location-aware-based intelligent parking guidance over infrastructureless VANETs, International Journal of Distributed Sensor Networks, vol. 2012.
[4]X. Zhang, D. Li, and J. Chen, 2014. Parking Space Reservation based on VANETs, International Journal of Advances in Management Science.
[5]Y. Ma, M. Chowdhury, A. Sadek, and M. Jeihani, 2012. Integrated traffic and communication performance evaluation of an intelligent vehicle infrastructure integration (VII) system for online travel-time prediction, IEEE Transactions on Intelligent Transportation Systems, vol. 13, pp. 1369-1382.
[6]R. P. D. Nath, H.-J. Lee, N. K. Chowdhury, and J.-W. Chang, 2010. Modified K-means clustering for travel time prediction based on historical traffic data, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, , pp. 511-521.
[7]R. Bauza and J. Gozálvez, 2013. Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications, Journal of Network and Computer Applications, vol. 36, pp. 1295-1307.
[1] D. Teodorović and P. Lučić, 2006. Intelligent parking systems. European Journal of Operational Research, vol. 175, pp. 1666-1681.
[2] H. Wang and W. He, A reservation-based smart parking system. in Computer Communications Workshops (INFOCOM WKSHPS), pp. 690-695. IEEE.
[3] H. Zhao, L. Lu, C. Song, and Y. Wu, 2012. IPARK: Location-aware-based intelligent parking guidance over infrastructureless VANETs, International Journal of Distributed Sensor Networks, vol. 2012.
[4] X. Zhang, D. Li, and J. Chen, 2014. Parking Space Reservation based on VANETs, International Journal of Advances in Management Science.
[5] Y. Ma, M. Chowdhury, A. Sadek, and M. Jeihani, 2012. Integrated traffic and communication performance evaluation of an intelligent vehicle infrastructure integration (VII) system for online travel-time prediction, IEEE Transactions on Intelligent Transportation Systems, vol. 13, pp. 1369-1382.
[6] R. P. D. Nath, H.-J. Lee, N. K. Chowdhury, and J.-W. Chang, 2010. Modified K-means clustering for travel time prediction based on historical traffic data, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, , pp. 511-521.
[7] R. Bauza and J. Gozálvez, 2013. Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications, Journal of Network and Computer Applications, vol. 36, pp. 1295-1307.
