The moving object or vehicle location prediction based on their spatial and temporal information is an important task in many applications. Different methods were utilized for performing the vehicle movement detection and prediction process. In such works, there is a lack of analysis in predicting the vehicles location in current as well as in future. Moreover, such methods compute the vehicles movement by finding the topological relationships among trajectories and locations, whereas the representative GPS points are determined by the 30 m circular window. Due to this process, the performance of the method is degraded because such 30 m circular window is selected by calculating the error range in the given input image and such error range may vary from image to image. To reduce the drawback presented in the existing method, in this study a heuristic moving vehicle location prediction algorithm is proposed. The proposed heuristic algorithm mainly comprises two techniques namely, optimization GA algorithm and FFBNN. In this proposed technique, initially the vehicles frequent paths are collected by monitoring all the vehicles movement in a specific period. Among the frequent paths, the vehicles optimal paths are computed by the GA algorithm. The selected optimal paths for each vehicle are utilized to train the FFBNN. The well trained FFBNN is then utilized to find the vehicle movement from the current location. By combining the proposed heuristic algorithm with GA and FFBNN, the vehicles location is predicted efficiently. The implementation result shows the effectiveness of the proposed heuristic algorithm in predicting the vehicles future location from the current location. The performance of the heuristic algorithm is evaluated by comparing the result with the RBF classifier. The comparison result shows our proposed technique acquires an accurate vehicle location prediction ratio than the RBF prediction ratio, in terms of accuracy.

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.

Co-location pattern discovery is intended towards the processing data with spatial contexts to discover classes of spatial objects that are frequently located together. The existing moving vehicle location prediction technique not analyses the moving vehicles co-location instance. So, we improve the previous technique process by mining spatially co-located moving objects using spatial data mining techniques. Initially, the neighbour relationship is computed by the prim's algorithm. After that, the candidate co-locations are pruned according to the presence of candidate co-location in the input data and the final stage of co-location instances selection is performed by compute neighbourhood and node membership functions. The values obtained using neighbourhood membership function is compared with the dynamic threshold values. The co-location instances are selected which satisfy the dynamic threshold value. Moreover, the proposed co-location pattern mining with dynamic thresholding technique is compared with the existing co-location pattern mining technique.