A Heuristic Moving Vehicle Location Prediction Technique via Optimal Paths Selection With Aid of Genetic Algorithm and Feed Forward Back Propagation Neural Network

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.

Electric vehicle range prediction for constant speed trip using multi-objective optimization

“…Sometimes, EA-based hybrid methods are also used to solve vehicle problems [2,26]. Meng et al [24] used extreme learning machines to obtain real-time Pareto-optimal solutions for an extended range EV based on objectives of IC engine efficiency, speed, and torque.…”

A Highway In-Transit Vehicle Position Estimation Method Considering Road Characteristics and Short-Term Driving Style

“…Mohammad S. Alzyout et al [35] introduced a short-term vehicle location prediction framework that enhances prediction accuracy and framework execution time by dynamically adjusting parameters and employing both multi-selective and single-selective ARIMA models. Baby Anitha et al [36] addressed the limitations of current methods in vehicle location prediction, which often lack analysis of both current and future vehicle positions and are affected by errors in GPS location data. They proposed a heuristic mobile vehicle location prediction algorithm, demonstrating that this heuristic algorithm can accurately predict the future positions of vehicles.…”

Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

“…The forecast is mainly for traffic status and clustering of road nodes at different times, thereby assisting vehicle position prediction. E Baby Anitha et al used genetic algorithms and backpropagated artificial neural networks as predictive training tools [22]. The path of the node is often calculated first, then the genetic algorithm is used to calculate the optimized path as the input of the neural network, and the model is trained to perform the position prediction of the vehicle node.…”