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Multivariate Traffic Forecasting Technique Using Cell Transmission Model and SARIMA Model

机译:基于信元传输模型和SARIMA模型的多元交通预测技术

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摘要

The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autore-gressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model-cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place.
机译:结合基于理论的季节自回归综合移动平均线(SARIMA)时间序列预测技术和基于理论的一阶宏观交通流模型-单元传输模型,开发了一种短期时空交通流预测策略。在都柏林市中心发生严重交通拥堵的案例研究中,测试了所提出的多元交通预测策略的有效性。结果表明,交叉路口的预测最多仅比原始观测值偏离大约10%,似乎表明所提出的策略是预测拥塞网络中实时流量水平的有效方法之一,尤其是在没有连续数据收集发生的位置。

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