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Saadallah/Moreira/2019a: BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract)

Bibtype Article
Bibkey Saadallah/Moreira/2019a
Author Saadallah, A., Moreira-Matias, L., Sousa, R., Khiari, J., Jenelius, E., Gama, J.
Ls8autor Saadallah, Amal
Title BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract)
Journal 35th IEEE International Conference on Data Engineering (ICDE 2019)
Abstract The dynamic behavior of urban mobility patterns
makes matching taxi supply with demand as one of the biggest
challenges in this industry. Recently, the increasing availability
of massive broadcast GPS data has encouraged the exploration
of this issue under different perspectives. One possible solution
is to build a data-driven real-time taxi-dispatching recommender
system. However, existing systems are based on strong assumptions
such as stationary demand distributions and finite training
sets, which make them inadequate for modeling the dynamic
nature of the network. In this paper, we propose BRIGHT: a
drift-aware supervised learning framework which aims to provide
accurate predictions for short-term horizon taxi demand quantities
through a creative ensemble of time series analysis methods
that handle distinct types of concept drift. A large experimental
set-up which includes three real-world transportation networks
and a synthetic test-bed with artificially inserted concept drifts,
was employed to illustrate the advantages of BRIGHT when
compared to S.o.A methods for this problem.
Index Terms—Time-series forecasting, Concept drift, Ensemble
learning, Taxi passenger demand.
Year 2019



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