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Road Traffic Prediction Using Artificial Neural Networks

机译:基于人工神经网络的道路交通预测

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摘要

The tremendous growth of the transportation systems and the increased number of vehicles during the last decades has created a significant problem in urban areas, that of traffic congestion. Traffic congestion increases the fuel consumption, causes air pollution and costs many hours per year to the drivers. In the current paper, a novel system targeted to predict the road traffic, using intelligent agents, is proposed. The accurate prediction of traffic will enable the road operators to proactively take appropriate measures, such as changing the traffic light strategy to alleviate the congestion problem. For the prediction process of the intelligent agents, artificial neural networks are employed in order to estimate the vehicles' speed on the road as an indicator of the traffic congestion. The results showed that the proposed system provides high accuracy with a mean absolute percentage error of about 6.2%.
机译:在过去的几十年中,运输系统的迅猛发展和车辆数量的增加在城市地区造成了交通拥堵的重大问题。交通拥堵增加了油耗,造成了空气污染,每年给驾驶员造成许多小时的损失。在当前的论文中,提出了一种使用智能代理来预测道路交通的新型系统。对交通的准确预测将使道路运营商能够主动采取适当措施,例如更改交通信号灯策略以缓解拥堵问题。对于智能主体的预测过程,采用人工神经网络来估计车辆在道路上的速度,作为交通拥堵的指标。结果表明,所提出的系统具有较高的精度,平均绝对百分比误差约为6.2%。

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