Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

Abstract

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice, the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackles both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

Accepted by AAAI 2019

References in corpus (3)

• Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
• GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
• DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Cited by in corpus (9)

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• D-GAN: Deep Generative Adversarial Nets for Spatio-Temporal Prediction
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• Localized convolutional neural networks for geospatial wind forecasting
• Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction
• Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
• GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
• GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series
• ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting