Author:
Priti Mishra and Poonam Bhogale
Abstract
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.
Publisher
International Journal for Modern Trends in Science and Technology (IJMTST)
Cited by
1 articles.
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