Bayesian Classifier for Route Prediction with Markov Chains

TL;DR

This paper introduces a Bayesian framework using Markov chains for predicting routes and destinations during ongoing journeys, demonstrating high accuracy on synthetic data.

Contribution

It presents a novel application of Markov chains within a Bayesian framework for real-time route prediction, building on previous hidden Markov model approaches.

Findings

High prediction accuracy demonstrated on synthetic datasets

Effective real-time updating of journey pattern probabilities

Framework adaptable to various route prediction scenarios

Abstract

We present here a general framework and a specific algorithm for predicting the destination, route, or more generally a pattern, of an ongoing journey, building on the recent work of [Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R. Shorten, and M. Mevissen, "Hidden Markov model for route and destination prediction," in IEEE International Conference on Intelligent Transportation Systems, 2017]. In the presented framework, known journey patterns are modelled as stochastic processes, emitting the road segments visited during the journey, and the ongoing journey is predicted by updating the posterior probability of each journey pattern given the road segments visited so far. In this contribution, we use Markov chains as models for the journey patterns, and consider the prediction as final, once one of the posterior probabilities crosses a predefined threshold. Despite the simplicity of both, examples run on a synthetic dataset demonstrate high accuracy of the made predictions.