In order to overcome the problems of low accuracy and time-consuming of traditional prediction methods for short-term traffic flow in urban, a prediction methods for short-term traffic flow in urban based on multiple linear regression model is proposed. The corresponding data attributes of short-term traffic flow in urban are selected by traffic operation status, and used as the original data of traffic flow prediction. According to the selected attributes, spatial static attributes data and traffic flow dynamic attributes data are collected, and fault data are identified and repaired. A multiple linear regression model for prediction of short-term traffic flow in urban is constructed to realize the prediction of short-term traffic flow in urban. The experimental results show that, compared with other methods, the average prediction accuracy of the proposed method is as high as 98.48%, and the prediction time is always less than 0.7 s, which is shorter.

中文翻译：

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基于多元线性回归模型的城市短期交通流量预测

为了解决传统的城市短期交通流量预测方法精度低，费时的问题，提出了一种基于多元线性回归模型的城市短期交通流量预测方法。根据交通运行状态选择城市短期交通流量的相应数据属性，并作为交通流量预测的原始数据。根据选择的属性，收集空间静态属性数据和交通流动态属性数据，并识别和修复故障数据。建立了用于预测城市短期交通流量的多元线性回归模型，以实现对城市短期交通流量的预测。实验结果表明，与其他方法相比，