In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to variousnon-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD.

中文翻译：

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基于并行CNN的大数据可视化流量监控

在流量监控等实时应用中，需要处理大量数据。交通预测对于智能交通系统（ITS），交通管理机构和旅行者而言至关重要。由于在不同位置的各种非线性时间动态，复杂的基础空间依赖性以及更扩展的阶跃预测，交通预测已成为一项具有挑战性的任务。为了适应这些情况，需要有效的可视化和数据挖掘技术来预测和分析大量流量大数据。本文提出了一种基于深度学习的并行卷积神经网络（Parallel-CNN）方法来预测特定区域的交通状况。深度学习的方法包含多个处理层并执行各种计算策略，这些学习策略用于学习具有多级抽象的数据表示。数据已从运输部门获取；因此，数据量巨大，可以对其进行分析以获得交通状况的行为。本文的目的是监视交通行为，从而使用户能够决定建设无交通城市。实验结果表明，所提出的方法优于其他现有方法，例如KNN，CNN和FIMT-DD。本文的目的是监视交通行为，从而使用户能够决定建设无交通城市。实验结果表明，所提出的方法优于其他现有方法，例如KNN，CNN和FIMT-DD。本文的目的是监视交通行为，从而使用户能够决定建设无交通城市。实验结果表明，所提出的方法优于其他现有方法，例如KNN，CNN和FIMT-DD。