Title: Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction,
Abstract: Taxi demand prediction is an important building block to enabling intelligent
transportation systems in a smart city. An accurate prediction model can help
the city pre-allocate resources to meet travel demand and to reduce empty taxis
on streets which waste energy and worsen the traffic congestion. With the
increasing popularity of taxi requesting services such as Uber and Didi Chuxing
(in China), we are able to collect large-scale taxi demand data continuously.
How to utilize such big data to improve the demand prediction is an interesting
and critical real-world problem. Traditional demand prediction methods mostly
rely on time series forecasting techniques, which fail to model the complex
non-linear spatial and temporal relations. Recent advances in deep learning
have shown superior performance on traditionally challenging tasks such as
image classification by learning the complex features and correlations from
large-scale data. This breakthrough has inspired researchers to explore deep
learning techniques on traffic prediction problems. However, existing methods
on traffic prediction have only considered spatial relation (e.g., using CNN)
or temporal relation (e.g., using LSTM) independently. We propose a Deep
Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial
and temporal relations. Specifically, our proposed model consists of three
views: temporal view (modeling correlations between future demand values with
near time points via LSTM), spatial view (modeling local spatial correlation
via local CNN), and semantic view (modeling correlations among regions sharing
similar temporal patterns). Experiments on large-scale real taxi demand data
demonstrate effectiveness of our approach over state-of-the-art methods.