# Bayesian Classifier for Route Prediction with Markov Chains

arXiv: https://arxiv.org/abs/1808.10705 · HTML: https://mlsanity.com/paper/1808.10705/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [A Unified Framework for Online Trip Destination Prediction](https://mlsanity.com/paper/2101.04520/) (2021)
- [Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs](https://mlsanity.com/paper/2303.15087/) (2023)
- [Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems](https://mlsanity.com/paper/2306.01282/) (2023)

## Abstract (arXiv)

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.