• Renuka Irkhede
• Dr. Nilesh Kasat

: A huge potential market for smart car applications is being created by recent advances in the vehicle industry and vehicle communication to everybody and mobility is very important in this area. This paper therefore proposes a new algorithm to support smart mobility applications for mobility of cars. First, a theoretical analysis reveals the predictability of mobility for vehicles quantitatively. Based on the knowledge gained from theoretical analyses, it is proposed to predict mobility of the vehicle in the future by using a deep recurrent neural network (RNN)-based algorithm known as Deep VM. The data has proved that Deep VM can significantly increase mobility forecast quality in comparison with other advanced algorithms and has proven the accuracy of its theoretical analysis. The results are also validated

Vehicle mobility, vehicle-to-everything (V2X), recurrent neural network, deep learning.

"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 8, page no.e47-e53, August-2021, Available :http://www.jetir.org/papers/JETIR2108487.pdf

"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 8, page no. ppe47-e53, August-2021, Available at : http://www.jetir.org/papers/JETIR2108487.pdf