Abstract: This paper presents Fuzzy Neural Network (FNN) based Adaptive Route Selection Support System (ARSSS) for assisting drivers of vehicles. The aim of the proposed ARSSS system is to select path based on shortest possible time. The proposed system intakes traffic information, such as volume to capacity ratio, traffic flow, vehicle queue length and green cycle length, passenger car unit etc using different types of sensor nodes, remote servers, CCTVs and the road information such as path length, signalized junctions, intersection points between source-destination pair are captured using GPS service. A FNN has been employed to select an optimal path having shortest time. The input parameters of FNN are distance, signal point delay, road type and traffic flow whereas the output parameter is path selection probability which paves the way to identify the best suitable path. The simulation result revels that FNN based ARSSS outperforms more accurate than that of other route selection support system (webster delay model) and artificial neural network (ANN) in estimating path delay.

Saoreen Rahman, M. Shamim Kaiser and Mahtab Uddin Ahmmed, “FNN based Adaptive Route Selection Support System” International Journal of Advanced Computer Science and Applications(IJACSA), 7(10), 2016. http://dx.doi.org/10.14569/IJACSA.2016.071048

@article{Rahman2016,
title = {FNN based Adaptive Route Selection Support System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.071048},
url = {http://dx.doi.org/10.14569/IJACSA.2016.071048},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {10},
author = {Saoreen Rahman and M. Shamim Kaiser and Mahtab Uddin Ahmmed}
}