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A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model
Ad Hoc Networks ( IF 4.8 ) Pub Date : 2020-05-30 , DOI: 10.1016/j.adhoc.2020.102224
Azzedine Boukerche , Jiahao Wang

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.



中文翻译:

基于混合机器学习的新型车辆交通流量预测系统的性能建模与分析

道路交通预测作为智能交通系统(ITS)的重要组成部分,最近引起了广泛关注。如何实现高效,强大和准确的车辆交通预测系统一直是热门话题之一。借助基于机器学习(ML)的方法,尤其是基于深度学习(DL)的方法,可以提高预测模型的准确性。但是,我们还注意到,在基于ML的车辆交通预测模型的实际实现中,仍然存在许多开放的挑战。首先,与参数模型(例如ARIMA,SARIMA)相比,训练DL模型的时间消耗相对较大。其次,如何捕获道路检测器之间的空间关系仍然是道路交通预测的热门话题,这受地理相关性以及时间变化的影响。最后但并非最不重要的一点,对我们来说,将预测系统应用于现实世界很重要;同时,我们应该找到一种方法,利用ITS中应用的先进技术来改进预测系统本身。在本文中,我们着重于改进预测模型的功能,这将有助于在现实世界中实现该模型。我们通过使用图卷积网络(GCN)和门控循环单元(GRU)的深度聚合结构(即序列到序列结构),提出了一种新的混合深度学习模型。同时,为了解决现实世界中的预测问题,即在线预测任务,我们通过细化学习提出了一种新的在线预测策略。

更新日期:2020-05-30
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