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An Efficient Optimization based Vehicle Movement Prediction with Aid of Feed Forward Back Propagation Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 91 - Number 12 |
| Year of Publication: 2014 |
| Authors: E. Baby Anitha, K. Duraiswamy |
10.5120/15933-5196
|
E. Baby Anitha, K. Duraiswamy . An Efficient Optimization based Vehicle Movement Prediction with Aid of Feed Forward Back Propagation Neural Network. International Journal of Computer Applications. 91, 12 ( April 2014), 24-31. DOI=10.5120/15933-5196
Abstract
Moving vehicle location prediction method mainly based on their spatial and temporal data . The moving objects has been developed as a specific research area of Geographic Information Systems (GIS). Most of the techniques have been used for performing the vehicle movement detection and prediction process. This type of work is a lack of analysis in predicting the moving vehicles location in current as well as in the future. Existing methods are using a Genetic Algorithm (GA) and Particle Swarm Optimization algorithm (PSO) for finding optimal paths in moving objects. Within the previous technique, there's no guarantee for fulfillment to finding a vehicle optimal path and also still now wants to improvement for choosing optimal path. To beat the disadvantage in the existing method, during this paper, to propose moving vehicle location prediction algorithm is an Artificial Bee Colony algorithm (ABC) and Feed Forward Back Propagation Neural Network (FFBNN). During this proposed algorithm is used for compute vehicle optimal path and selected optimal paths are given to the FFBNN to accomplish the training process. The trained FFBNN is then used to find the vehicle moving from the current location. By combining ABC algorithm and FFBNN, the moving vehicle's location is predicted more efficiently. The outcomes of the FFBNN-ABC algorithm are compared with results of previous method, such as FFBNN-GA, FFBNN-PSO. The evaluation result shows that the proposed technique more accurate than other algorithms.
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