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Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding

Title
Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding
Authors
LEE, DOYUPJUNG, SUEHUNCHEON, YEONGJAEKIM, DONGILYOU, SEUNGIL
Date Issued
7-Dec-2018
Publisher
Neural Information Processing Systems Foundation
Abstract
Learning complex spatiotemporal patterns is a key to predict future taxi demand volumes. We propose temporal guided networks (TGNet), which is an efficient model architecture with fully convolutional networks and temporal guided em- bedding, to capture spatiotemporal patterns. Existing approaches use complex architectures, historical demands (day/week/month ago) to capture the recurring patterns, and external data sources such as meteorological, traffic flow, or tex- ture data. However, TGNet only uses fully convolutional networks and temporal guided embedding without those external data sources. In this study, only pick-up and drop-off volumes of NYC-taxi dataset are used to utilize the full potential of the hidden patterns in the historical data points. We show that TGNet provides notable performance gains on a real-world benchmark, NYC-taxi dataset, over previous state-of-the-art models. Finally we explain how to extend our architecture to incorporate external data sources.
URI
http://oasis.postech.ac.kr/handle/2014.oak/94544
Article Type
Conference
Citation
Thirty-second Conference on Neural Information Processing Systems, 2018-12-07
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