Traffic Flow Prediction With Big Data: A Deep Learning Approach
Traffic Flow Prediction With Big Data: A Deep Learning Approach
复制标题
大数据交通流量预测：深度学习方法
DOI：
10.1109/tits.2014.2345663
复制
发表时间：
2015-04-01
影响因子：
8.5
通讯作者：
Wang, Fei-Yue
中科院分区：
文献类型：
--
作者：
Lv, Yisheng;Duan, Yanjie;Wang, Fei-Yue
准确、及时的交通流量信息对于智能交通系统的成功部署至关重要。在过去的几年里，交通数据呈爆炸式增长，我们已经真正进入了交通大数据时代。现有的交通流预测方法主要采用浅层交通预测模型，在实际应用中仍不能令人满意。这种情况促使我们重新思考基于大交通数据的深度架构模型的交通流预测问题。本文提出了一种基于深度学习的交通流预测方法，该方法综合考虑了交通流的时空相关性。采用层叠式自编码器模型学习通用交通流特征，并采用贪婪分层的方式进行训练。据我们所知，这是第一次应用深度架构模型，使用自动编码器作为构建块来表示用于预测的交通流特征。实验结果表明，该方法具有较好的交通流预测性能。
Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.