Forecasting traffic flow by vehicle category on a major highway impacted by road maintenance works

In this paper we look at the forecasting of traffic flow on a major highway in the Netherlands impacted by road maintenance works, examining the effects of lane closures on intensities per vehicle category. We apply several forecasting methodologies such as Prophet, Harmonic Regression, Seasonal Autoregressive (SAR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) and compare them against a seasonal naive baseline model. We observe that SARIMA performs better than other models across all forecasting metrics for all sensors. This is mainly because of its capability of capturing linear trends and seasonality. There is also an opportunity to further improve the forecast accuracy of the SARIMA model by incorporating holiday information as seen in the Prophet model. Overall, the analysis showed that road works and holidays are two features that have more influence on traffic flow, which should be considered as main factors when carrying out future road plans. If multiple areas are affected, the K-means model can be adopted effectively to cluster the sensors into groups to minimize traffic disruption.

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