- 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 embedding, 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 texture 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.
- Keywords: deep learning, traffic modeling, forecasting, taxi demand, fully convolutional networks, temporal guided embedding, spatiotemporal modeling
- TL;DR: We propose an efficient model architecture, TGNet, which is fully convolutional networks with temporal guided embedding. TGNet shows state-of-the-art performance on taxi demand forecasting, without external data sources.