DeepVM: RNN-based Vehicle Mobility Prediction to Support Intelligent
Vehicle Applications
Abstract
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 DeepVM 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.