2018/08/31 by Jonathan P. Epperlein, Julien Monteil, Epperlein, Jonathan P. +9
Computer Science · Engineering · Mathematics · Social Sciences · #Data Management and Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR) #Traffic Prediction and Management Techniques #cs.LG #math.PR #stat.ML
paper · pdf · doi:10.48550/arxiv.1808.10705
Accepted at The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC)
arxiv created 2018/08/31 · openalex publication_date 2018/08/31 · arxiv updated 2018/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.