Metro system has been increasingly recognized as a backbone of urban transportation system in many cities around the world. To improve the demand management and operation efficiency, it is crucial to have accurate prediction of real-time metro passenger flow. However, the forecast performance is often subject to the complex spatial and temporal distributions of the metro passenger flow data. To this end, we developed a novel Dual Attentive Graph Neural Network (DAGNN) that can effectively predict the distribution of metro traffic flow considering the spatial and temporal influences. Specifically, two directed complete metro graphs (i.e., inbound and outbound graphs) and the weighted matrix of them are proposed to characterize the inbound (entering the system) and outbound (leaving the system) passenger flow respectively. The weighted matrix of inbound graph is estimated based on the historical origin-destination (OD) demand and that of the outbound graph is estimated based on the similarity metrics between every two stations. Moreover, to capture the dependencies between inbound and outbound flows, multi-layer Graph Spatial Attention Networks (GSANs) that incorporate the spatial context are applied to exploit the dynamic inter-station correlations. Then, the acquired dependencies feature integrated with external factors, such as weather conditions, are filtered by temporal attention and fed into a sequence decoder to produce short-term and longterm passenger flow predictions. Finally, a series experiments are conducted based on a comprehensive empirical dataset. Findings indicated that the proposed model does not only well predict the metro passenger flow, but also effectively detect the emergencies and incidents of metro system.
Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole city, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data (FCD).
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