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SHORT-TERM TRAFFIC FLOW PREDICTION USING ARTIFICIAL INTELLIGENCE WITH PERIODIC CLUSTERING AND ELECTED SET

机译：SHORT-TERM TRAFFIC FLOW PREDICTION USING ARTIFICIAL INTELLIGENCE WITH PERIODIC CLUSTERING AND ELECTED SET

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Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Oustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.
机译：使用历史数据预测短期交通流量是由于事件随机性实现的难度目标。由于短期交通预测缺乏坚实的方法，研究人员仍在研究新的方法。本研究旨在开发一种动态更新培训模型集的算法，以便更准确的预测。为此目的，已经开发了一种称为周期性跟踪和预测（PCP）的算法，用于短期交通预测。在本研究中，PCP用于通过改进ANN训练集来改进人工神经网络（ANN）预测性能，以预测使用所选集群的短期交通流量。从美国和英国高速公路收集的大量交通数据用于确定PCP增加ANN绩效的能力。所提出的方法的稳健性取决于文献中使用的性能措施，PCP的平均预测误差显着低于其他方法。此外，研究表明，响应于增加的交通流量值，PCP预测的百分比误差降低。考虑到所获得的阳性结果，该方法可用于实时交通管制系统和所需的不同区域。

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