Deep Learning-Based Destination Prediction Scheme by Trajectory Prediction Framework

Abstract

Destination prediction is an important task in vehicular ad hoc networks (VANETs), which benefits to resolve traffic congestion. The traditional destination prediction methods are mainly based on statistical learning, which is difficult to fit the movement pattern of vehicles and predict destination of a new query trajectory. In recent years, deep learning attracted more and more attentions since it has a better ability to adaptively extract features from data and fit complex models. The exiting deep learning-based destination prediction methods mainly extract features from past trajectory and fail to exploit future trajectory because the future locations are unknown, which results in a large prediction error. In this paper, we propose a novel deep learning-based destination prediction model that can predict the future trajectory and make full use of the information about it. Firstly, we exploit a trajectory prediction method to predict future locations, which makes the process of destination prediction continuous. Secondly, we design a branch network to determine which future location is the destination of the trajectory. We also evaluate our model with a real-world dataset. Experimental results show that our model can predict the coordinates of destination with a low prediction error.