A unified framework for online trip destination prediction
Journal article, 2022

Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines.

Expert model

Bayesian prediction


Regret analysis

Online learning

Trip destination prediction


Victor Ebberstein

Chalmers, Mechanics and Maritime Sciences (M2), Combustion and Propulsion Systems

Jonas Sjöblom

Chalmers, Mechanics and Maritime Sciences (M2), Combustion and Propulsion Systems

Nikolce Murgovski

Chalmers, Electrical Engineering, Systems and control

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Machine Learning

0885-6125 (ISSN) 1573-0565 (eISSN)

Vol. In Press

Subject Categories

Other Computer and Information Science

Computer Science



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9/1/2022 1