• RENUKA IRKHEDE
• Dr. Nilesh Kasat

The recent advances in vehicle industry and vehicle to-everything communications are creating a huge potential market of intelligent vehicle applications, and exploiting vehicle mobility is of great importance in this field. Hence, this paper proposes a novel vehicle mobility prediction algorithm to support intelligent vehicle applications. First, a theoretical analysis is given to quantitatively reveal the predictability of vehicle mobility. Based on the knowledge earned from theoretical analysis, a deep recurrent neural network (RNN)-based algorithm called Deep VM is proposed to predict vehicle mobility in a future period of several or tens of minutes. Comprehensive evaluations have been carried out based on the real taxi mobility data in Tokyo, Japan. The results have not only proved the correctness of our theoretical analysis, but also validated that DeepVM can significantly improve the quality of vehicle mobility prediction compared with other state-of-art algorithms.

"Review on “Vehicle Mobility Prediction for Intelligent Application by Using RNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.e837-e843, July-2021, Available :http://www.jetir.org/papers/JETIR2107611.pdf

"Review on “Vehicle Mobility Prediction for Intelligent Application by Using RNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppe837-e843, July-2021, Available at : http://www.jetir.org/papers/JETIR2107611.pdf