How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
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The huge success of deep learning in computer vision and natural language processing has inspired researchers to exploit deep learning techniques in traffic domain. Various deep learning architectures have been proposed to solve the complex challenges (e.g., spatial temporal dependencies) in traffic domain. In addition, researchers traditionally modeled the traffic network as grids or segments in spatial dimension. However, many traffic networks are graph-structured in nature. In order to utilize such spatial information fully, it's more appropriate to formulate traffic networks as graphs mathematically. Recently, many novel deep learning techniques have been developed to process graph data. More and more works have applied these graph-based deep learning techniques in various traffic tasks and have achieved state-of-the art performances. To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in many traffic applications. We first give guidelines to formulate a traffic problem based on graph and construct graphs from various traffic data. Then we decompose these graph-based architectures and discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks. What's more, we summarize common traffic challenges and the corresponding graph-based deep learning solutions to each challenge. Finally, we provide benchmark datasets, open source codes and future research directions in this rapidly growing field.
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