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VDS Data-Based Deep Learning Approach for Traffic Forecasting Using LSTM Network

机译:使用LSTM网络的基于VDS数据的深度学习方法进行流量预测

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Traffic forecasting is an important component of the Intelligent Transportation System (ITS). Recently, deep learning has been introduced as a promising method for traffic forecasting to deal with the exponential growth of data in ITS. In this regard, this paper focuses on applying a deep neural network model using LSTM for traffic forecasting based on analyzing data from the Vehicle Detection System (VDS). In particular, we first try to understand the traffic condition by applying visualization techniques. Then, based on the traffic condition, we apply an appropriate deep learning model for predicting traffic flow. Experiments in a certain urban area present promising results by applying the proposed model.
机译:交通预测是智能交通系统(ITS)的重要组成部分。最近,深度学习已被引入作为一种有前途的流量预测方法,以处理ITS中数据的指数增长。在这方面,本文重点研究基于LSTM的深度神经网络模型,该模型基于对来自车辆检测系统(VDS)的数据进行分析来进行交通预测。特别是,我们首先尝试通过应用可视化技术来了解交通状况。然后,根据交通状况,我们应用适当的深度学习模型来预测交通流量。通过应用所提出的模型,在某些城市地区的实验提出了可喜的结果。

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