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潘哲逸-Urban Traffic Prediction from Spatio Temporal Data Using Deep Meta Learning
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城市交通预测是城市计算领域中一个非常重要的研究课题,准确的交通预测可以帮助我们更好地理解城市交通,给交通系统的改进提供思路,同时也能对民众提供及时的城市交通预警。然而,准确的城市交通预测需解决以下两个挑战:1) 交通数据间复杂的时空相关性,即一个地点的交通状况会影响其未来一段时间内的交通,也会影响其周围区域的交通。2) 不同地点间,数据的时空相关性是多样的,并且这样的相关性依赖于地理信息,如地点周围的兴趣点,路网结构等。为了解决这两个挑战,我们提出了一个基于深度元学习的模型:ST-MetaNet
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