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A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model

机译：基于混合机学习模型的新型车辆交通流量预测系统的性能建模与分析

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Traffic prediction on the road, as a vital part of the Intelligent Transportation System (ITS) has attracted much attention recently. It is always one of the hot topics about how to implement an efficient, robust, and accurate vehicular traffic prediction system. With the help of Machine Learning-based (ML) methods, especially Deep Learning-based (DL) methods, the accuracy of the prediction model is increased. However, we also noticed that there are still many open challenges under ML-based vehicular traffic prediction model real-world implementation. Firstly, the time consumption for training DL model is relatively large when compared to parametric models, such as ARIMA, SARIMA. Second, it is still a hot topic for road traffic prediction that how to capture the spacial relationship between road detectors, which is affected by the geographic correlation, as well as the time change. The last but not the least, it is important for us to implement the prediction system into the real world; meanwhile, we should find a way to make use of the advanced technology applied in ITS to improve the prediction system itself. In this paper, we focus on improving the features of the prediction model, which can be helpful for implementing the model in the real world. We present a new hybrid deep learning model by using Graph Convolutional Network (GCN) and the deep aggregation structure (i.e., the sequence to sequence structure) of Gated Recurrent Unit (GRU). Meanwhile, in order to solve the real-world prediction problem, i.e., the online prediction task, we present a new online prediction strategy by using refinement learning. In order to further improve the model's accuracy and efficiency when applied to ITS, we make use of an efficient parallel training strategy while taking advantage of the vehicular cloud structure. (C) 2020 Elsevier B.V. All rights reserved.
机译：道路上的交通预测是智能交通系统的重要组成部分（其）最近引起了很多关注。它始终是如何实现高效，鲁棒和准确的车辆流量预测系统的热门话题之一。借助基于机器学习的（ML）方法，特别是深度学习（DL）方法，预测模型的准确性增加。然而，我们还注意到，基于ML的车辆交通预测模型现实世界实施方面仍然存在许多开放挑战。首先，与参数模型相比，训练DL模型的时间消耗相对较大，例如Arima，Sarima。其次，这仍然是道路交通预测的热门话题，如何捕获道路探测器之间的间隔关系，这是受地理相关影响的，以及时间变化。最后但并非最不重要的是，我们对我们将预测系统实施到现实世界中很重要;与此同时，我们应该找到一种方法来利用其应用中的先进技术来改善预测系统本身。在本文中，我们专注于改善预测模型的特征，这有助于实现现实世界中的模型。我们通过使用Graph卷积网络（GCN）和门控复发单元（GU）的深度聚集结构（即，序列结构）来提出新的混合深度学习模型。同时，为了解决现实世界的预测问题，即在线预测任务，我们通过使用细化学习来提出新的在线预测策略。为了进一步提高模型的准确性和效率，当应用于其时，我们利用了高效的平行训练策略，同时利用了车辆云结构。 （c）2020 Elsevier B.v.保留所有权利。

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