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首页> 外文期刊>Promet-traffic & transportation >EFFECT OF TIME INTERVALS ON K-NEAREST NEIGHBORS MODEL FOR SHORT-TERM TRAFFIC FLOW PREDICTION
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EFFECT OF TIME INTERVALS ON K-NEAREST NEIGHBORS MODEL FOR SHORT-TERM TRAFFIC FLOW PREDICTION

机译：时间间隔对短期交通流预测k最近邻居模型的影响

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摘要

The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, we extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval.
机译：预测短期交通流量的准确性和可靠性很重要。 K最近邻居（K-NN）方法已被广泛用作交通流量预测的非参数模型。然而，K-Nn模型结果的可靠性未知，并且需要量化交通流点预测的不确定性。为此，我们通过构造与点预测相关联的预测间隔来扩展K-NN方法。识别交通的随机性质，用于测量交通流量的时间间隔非常有影响。在本文中，在将真正的交通流数据聚集到时间间隔之后，也进行了广泛的测试，从3分钟到30分钟。结果表明，当时间间隔增加时，可以提高交通流量预测的性能。更重要的是，当时间间隔短于10分钟时，K-Nn可以产生比所选择的基准模型的点预测的更高精度。该发现表明K-NN模型可能更适合于交通流点和间隔预测以较短的时间间隔。

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