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首页> 外文会议>International Joint Conference on Neural Networks >Deep Convolutional Neural Networks with Random Subspace Learning for Short-term Traffic Flow Prediction with Incomplete Data
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Deep Convolutional Neural Networks with Random Subspace Learning for Short-term Traffic Flow Prediction with Incomplete Data

机译：具有不完整数据的短期交通流量预测的带有随机子空间学习的深度卷积神经网络

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摘要

Traffic flow prediction is a fundamental component in intelligent transportation systems. However, many existing prediction models endure several shortages. Most of the methods are constructed as a shallow model, which is difficult to reveal the intrinsic spatio-temporal relations embedded in traffic raw data. Moreover, the separation of feature learning and predictor learning brings a sacrifice of model performance. Then the hand designed features are difficult to be tuned appropriately. Finally, few existing methods consider the incomplete data problem which is in fact very severe for practical application. In this work, we develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that integrates random subspace learning and ensemble learning on deep convolutional neural networks. The proposed model takes the traffic flow data as an image, and considers both exploring spatio-temporal correlations in the unified architecture and the incomplete data problem. The experimental results, using traffic data originated from the California Freeway Performance Measurement System (PeMS), corroborate the effectiveness of the proposed approach compared with the state of the art.
机译：交通流量预测是智能交通系统中的基本组成部分。但是，许多现有的预测模型都存在一些不足。大多数方法被构建为一个浅层模型，很难揭示嵌入在交通原始数据中的内在时空关系。此外，特征学习和预测器学习的分离带来了模型性能的牺牲。这样，手工设计的功能很难进行适当的调整。最后，很少有现有的方法考虑不完整的数据问题，实际上这对于实际应用是非常严重的。在这项工作中，我们开发了一种深度学习模型来预测流量。主要贡献是开发了一种在深度卷积神经网络上集成了随机子空间学习和集成学习的体系结构。所提出的模型以交通流数据为图像，并考虑了在统一体系结构中探索时空相关性和不完整数据问题。使用源自加利福尼亚高速公路绩效评估系统（PeMS）的交通数据得出的实验结果，与现有技术相比，证实了所提出方法的有效性。

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