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Short term road traffic flow forecasting using multi layer perceptron neural networks

机译:使用多层Perceptron神经网络的短期道路交通流预测

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摘要

In recent days, road traffic management and congestion control has become major problems in any busy junction in Hyderabad city. Hence short term traffic flow forecasting has gained greater importance in Intelligent Transport System(ITS). Artificial Neural Network(ANN) models have been fruitfully applied for classification and prediction of time series. In this paper, an attempt has been made to model and forecast short-term traffic flow at 6.no. junction in Amberpet, |oHyderabad, Telangana state, India applying Neural Network models. The traffic data has been considered for peak hours in the morning for 8A.M to 12 Noon, for 5 days. Multilayer Perceptron (MLP) network model is used in this study. These results can be considered to monitor traffic signals and explore methods to avoid congestion at that junction.
机译:最近几天,道路交通管理和拥挤控制已成为海德拉巴城市任何繁忙交界处的主要问题。 因此,短期交通流预测在智能运输系统(其)中获得了更重要的意义。 人工神经网络(ANN)模型已经效果效果应用于时间序列的分类和预测。 在本文中,已经尝试在6.No.no.的模拟和预测短期交通流量。 Amberpet的Junction,| Ohyderabad,Telangana State,印度应用神经网络模型。 交通数据已被认为是早上8:12中午的最高时光,5天。 本研究中使用多层erceptron(MLP)网络模型。 这些结果可以考虑监控交通信号并探索避免在该交界处拥塞的方法。

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