A comparison of approaches for the time series forecasting of motorway traffic flow rate at hourly and daily aggregation levels
Congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. Congestion is generally associated with reduced average speed at a high traffic flow rate. This traffic flow rate is defined as the number of vehicles that pass a certain location at a given time. The modelling and prediction of this traffic flow rate may lead to valuable insights that may be used to reduce congestion and societal costs. This study aims to predict the traffic flow rate for 41 different locations in and around Amsterdam, The Netherlands. Using TBATS, SARIMAX and LSTM models, among others, the traffic flow rate of these locations has successfully been modelled. These models may provide accurate predictions for the future flow rate, which may be useful for the identification of infrastructure bottlenecks and the scheduling of maintenance. Considering the dramatic effects of the COVID-19 pandemic on the traffic flow rate, the inclusion of 2020 data with a number of external factors, could lead to an improvement of the results and the ability to model the future effects of the pandemic.
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|13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands|
van der Bijl, B, Gijsbertsen, B, van Loon, S, Reurich, Y, de Valk, T, Koch, T, & Dugundji, E.R. (2022). A comparison of approaches for the time series forecasting of motorway traffic flow rate at hourly and daily aggregation levels. In Procedia Computer Science (pp. 213–222). doi:10.1016/j.procs.2022.03.030