Abstract
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.
Highlights
Accommodated into data mining algorithms (Roddick et al, 2004)
All the vehicles frequent moving paths are collected and optimal frequent paths of each vehicle are computed by the Genetic Algorithm (GA) optimization technique
We find all the vehicles optimal frequent paths and these optimal paths of each vehicle are trained in Feed Forward Back Propagation Neural Network (FFBNN)
Summary
Accommodated into data mining algorithms (Roddick et al, 2004). Spatial data mining comes into picture when the. The prediction of moving objects location based on the time series is a significant task in several applications such as wireless based, location-aware devices and networks of sensors and traffic analysis. The study of the topological changes of spatial objects over time, i.e., of time dependent geometries called moving objects, is important in several applications such as Geographical Information Systems (GIS), spatiotemporal databases, the processing of animation images in multimedia applications and the topology control of Wireless Sensor Networks (WSN) (Liu and Schneider, 2010). With embedded GPS devices and other sensors the tracking of moving objects, whether it is a tiny cell phone or a giant ocean liner, is becoming increasingly accessible Such enormous amount of data on moving objects poses immense challenges on effective and scalable analysis of such data and exploration of its applications (Li et al, 2006). It is important to know the approximate position of a mobile user in order to operate it, for this the knowledge about the positions of mobile objects has led to the location-based services and applications (Monreale et al, 2009)
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