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Application of the ARIMAX Model on Forecasting Freeway Traffic Flow

机译：ARIMAX模型在高速公路交通流量预测中的应用

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摘要

Real-time traffic flow forecasting is of vital significance to the intelligent transportation system (ITS). Providing real-time and efficient information for travellers, the forecast aims to reduce travel times, control traffic pressure, and reduce pollution. The time-series autoregressive integrated moving average (ARIMA) model has been widely used in advanced traffic management systems. Compared to the traditional ARIMA model, the autoregressive integrated moving average with exogenous inputs (ARIMAX) model can take the impact of covariates on the forecasting into account to improve the comprehensiveness and accuracy of the prediction. Based on data accessibility and correlation analysis, the upstream traffic flow is taken as the covariate X of the model. Data was collected from Interstate Highway 280 in California, with a sampling period of 5 minutes. The results showed that the ARIMAX model outperforms the ARIMA model during morning peak hours in terms of accuracy and comprehensiveness.
机译：实时交通流量预测对于智能交通系统（ITS）至关重要。该预测为旅客提供实时，高效的信息，旨在减少出行时间，控制交通压力并减少污染。时间序列自回归综合移动平均（ARIMA）模型已广泛用于高级交通管理系统中。与传统的ARIMA模型相比，带有外部输入的自回归综合移动平均值（ARIMAX）模型可以考虑协变量对预测的影响，从而提高预测的全面性和准确性。基于数据可访问性和相关性分析，将上游流量作为模型的协变量X。数据是从加利福尼亚州280号州际公路收集的，采样时间为5分钟。结果表明，在准确性和综合性方面，ARIMAX模型在早晨高峰时段优于ARIMA模型。

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