Short-Term Traffic Forecasting on Highways Using Neural Networks
A Congestion Pattern-Based Assessment
K. Simons (TU Delft - Civil Engineering & Geosciences)
J.W.C. van Lint – Graduation committee member (Transport and Planning)
P.K. Krishnakumari – Graduation committee member (Transport and Planning)
M.M. de Weerdt – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C. Achterberg – Graduation committee member (CGI )
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Abstract
As traffic demands are ever increasing and building new infrastructure poses challenges in densely populated areas, it is important to optimally utilise existing infrastructure. Short-term traffic forecasting can help with this task, as its predictions can help to prevent congestion by rerouting vehicles. Recently, neural networks developed for traffic forecasting have lead to an unprecedented accuracy, but deploying these on large scale can be difficult as the resulting models likely overfit to the highway they were trained on. This thesis therefore performs an in-depth assessment of the accuracy of neural networks traffic speed predictions on highway stretches containing different types of congestion patterns. By relating the results back to traffic flow theory, these results can be put into perspective.