An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus

Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service; however, it is challenging. On the other hand, the stateless predictive model is unsuitable due to less accuracy and mismatch pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. Firstly, this paper comprehensively analyzes the recent research on vehicle location prediction models and provides a complete taxonomy. The proposed model uses the Support Vector Regression (SVR) based vehicle location predictive model in the implementation phase. In addition, a significant time-series predictive model, ARIMA, is configured and tested. Moreover, the seasonal ARIMA model is experimented with to predict the location of a mobile vehicle. The whole experiment is performed on a real dataset based on the university shuttle bus service. The results of the experiments, analysis, and discussions on the proposed model show the accuracy and effectiveness of their use on others. As a result, seasonal ARIMA outperformed ARIMA and SVR infractions.