Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.
复制标题
将交通作为图像进行学习：用于大规模交通网络速度预测的深度卷积神经网络
DOI：
10.3390/s17040818
复制
发表时间：
2017-04-10
期刊：
影响因子：
--
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Ma X;Dai Z;He Z;Ma J;Wang Y;Wang Y
本文提出了一种基于卷积神经网络（CNN）的方法，该方法将流量作为图像进行学习，并以高精度预测大规模的网络范围内的流量速度。通过二维时空矩阵将时空交通动力学转化为描述交通流时空关系的图像。CNN在两个连续步骤之后应用于图像：抽象交通特征提取和网络范围的交通速度预测。以北京市二环路和东北交通网络两个实际交通网络为例，将该方法与普通最小二乘、k-近邻、人工神经网络和随机森林等四种主流算法以及堆栈式自编码器、递归神经网络、和长短期记忆网络。实验结果表明，在可接受的执行时间内，该方法的平均准确率提高了42.91%，优于其他算法。CNN可以在合理的时间内训练模型，因此适用于大规模的交通网络。
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.