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A neural network approach for predicting speeds on road networks

机译：一种用于预测道路网络速度的神经网络方法

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摘要

It is possible for routing and navigation applications to provide more accurate and more effective route planning solutions by accurately predicting the traffic density or vehicle speed. Numerous methods and approaches have been studied to achieve this objective; however, they have mainly focused on the short-term traffic prediction. In addition, the studies that attempt to provide mid-and long-term predictions tend to show unacceptable accuracy levels. In this study, we employ Artificial Neural Networks (ANN). They will combine the predictions made by various time series forecasting methods to make mid-and long-term speed predictions. In the experimental study, we utilize floating car speed data on two routes collected by GPS devices with 1-minute intervals over a five month-period. The results reveal the superior performance of ANN and show that it provides accurate predictions over a 30-minute time interval.
机译：路由和导航应用可以通过准确地预测交通密度或车速来提供更准确和更有效的路线规划解决方案。已经研究了许多方法和方法来实现这一目标;但是，它们主要集中在短期交通预测上。此外，试图提供中期预测和长期预测的研究倾向于表现出不可接受的精度水平。在这项研究中，我们采用人工神经网络（ANN）。它们将结合各种时间序列预测方法所做的预测，以使中期和长期速度预测。在实验研究中，我们利用GPS器件收集的两条路线上的浮动车速数据，在五个月的时间内为1分钟间隔。结果揭示了ANN的卓越性能，并表明它提供了30分钟时间间隔的准确预测。

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