BRIGHT - Drift-Aware Demand Predictions for Taxi Networks

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Saadallah, A (Author) Other; Moreira Matias, L (Author) Other; Sousa, R (Author) Other; Khiari, J (Author) Other; Jenelius, E (Author) Other; João Gama (Author) FEP

Journal

Title: IEEE Transactions on Knowledge and Data Engineering

Vol. 32 No. 2

Pages: 234-245

ISSN: 1041-4347

Publisher: IEEE

Indexing

ISI Web of Knowledge - 21 Citations

Scopus - 24 Citations

Other information

Authenticus ID: P-00Q-ES5

DOI: 10.1109/tkde.2018.2883616

Abstract (EN): Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China) and Stockholm (Sweden), as well as controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task. IEEE

Language: English

Type (Professor's evaluation): Scientific