Destination Prediction by Trajectory Distribution Based Model

TL;DR

This paper introduces a novel trajectory distribution model that predicts vehicle trip destinations using initial trajectory data, clustering, and Gaussian mixture models, validated on taxi GPS datasets.

Contribution

The paper presents a new method combining clustering and Gaussian mixture models for accurate destination prediction from partial trajectories.

Findings

Effective destination prediction on taxi GPS data

Automatic adaptation to different datasets

Improved trajectory classification accuracy

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 behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d 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 mot likely belongs. Secondly, 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 datasets of timestamped GPS-Location of taxi trips. We test our methods on two different datasets, to assess the capacity of our method to adapt automatically to different subsets.