Hybrid Ensemble-Based Travel Mode Prediction

TL;DR

This paper introduces IEBSM, a novel ensemble method combining batch and stream models with drift detection to improve travel mode prediction accuracy amid evolving data, outperforming existing methods.

Contribution

The paper proposes a new Incremental Ensemble of Batch and Stream Models (IEBSM) for adaptive travel mode prediction addressing concept drift.

Findings

IEBSM effectively detects drift in travel mode data.

IEBSM adapts models successfully to evolving data.

IEBSM outperforms batch and stream learners in experiments.

Abstract

Travel mode choice (TMC) prediction, which can be formulated as a classification task, helps in understanding what makes citizens choose different modes of transport for individual trips. This is also a major step towards fostering sustainable transportation. As behaviour may evolve over time, we also face the question of detecting concept drift in the data. This necessitates using appropriate methods to address potential concept drift. In particular, it is necessary to decide whether batch or stream mining methods should be used to develop periodically updated TMC models. To address the challenge of the development of TMC models, we propose the novel Incremental Ensemble of Batch and Stream Models (IEBSM) method aimed at adapting travel mode choice classifiers to concept drift possibly occurring in the data. It relies on the combination of drift detectors with batch learning and stream mining models. We compare it against batch and incremental learners, including methods relying on active drift detection. Experiments with varied travel mode data sets representing both city and country levels show that the IEBSM method both detects drift in travel mode data and successfully adapts the models to evolving travel mode choice data. The method has a higher rank than batch and stream learners.