Location Tracking Web Application Using Machine Learning

Many applications in intelligent transportation systems are demanding an accurate web application- based location prediction. In this study, we satisfy this demand by designing an automated mobile user location prediction system based on the well-known traditional Auto-Regressive Integrated Moving Average (ARIMA). To increase the proposed model accuracy, make it dynamic, and reduce its execution time, the traditional ARIMA model has been modified extensively by using different combinations of design options of the model. To perform user location prediction, the proposed model depends the previous recorded user locations to predict the user future locations. To make the proposed model dynamic, it is designed to regenerate all its parameters periodically. To deal with such dynamic environment, only a specified window of the historical data is used. To reduce the regeneration of the model execution time, the model selection process is enhanced and several model selection approaches are proposed. The proposed model and the different design options are evaluated using a realistic user location dataset trace that are recorded using a WIFI embedded, as well as, using traces from a previous study called the Kaggle Dataset. To deal with any imperfection in the data used in generating the model in this study. The results show that the proposed framework can generate ARIMA models that can predict the future user locations of a user accurately and with a reasonable execution time. The results also show that the proposed model can predict the user’s location for several future steps with an acceptable accuracy with 90%.