A Unified Framework for Online Trip Destination Prediction

TL;DR

This paper introduces a unified online framework for trip destination prediction, combining clustering and prediction models, demonstrating improved convergence and a novel regret metric to evaluate online versus offline performance.

Contribution

It presents a novel unified online framework with clustering and prediction models, and introduces a regret metric for better evaluation of online trip destination prediction.

Findings

Framework yields results comparable to offline methods

Proposed regret metric effectively distinguishes sources of errors

Methods converge to true distribution with lower regret than baselines

Abstract

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.