Destination Prediction by Trajectory Distribution-Based Model

Abstract

In this paper, we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behavior. Then, we explain how we model main traffic flow patterns by a mixture of 2-D Gaussian distributions. This yielded a density-based clustering of locations, which produces a data driven grid of similar points within each pattern. We present how this model can be used to predict the final destination of a new trajectory based on their first locations using a two-step procedure: we first assign the new trajectory to the clusters it most likely belongs. Second, we use characteristics from trajectories inside these clusters to predict the final destination. Finally, we present experimental results of our methods for classification of trajectories and final destination prediction on data sets of timestamped GPS-Location of taxi trips. We test our methods on two different data sets, to assess the capacity of our method to adapt automatically to different subsets.