Predicting the Effect of an Accident on Traffc Congestion
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2018-07-12
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en
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Abstract
In this study a Multilayer Perceptron was applied to predict tra c jams based on accidents.
Emergency service messages were used as an indication of an accident. The model uses emergency
service messages and road tra c information as input features. A dataset was created that com-
bines information about accidents with tra c data. The model predicts if an accident is followed
by a tra c jam within the next 15 minutes. The results show that a higher AUC than the base-
line of 0.5 is achievable. Furthermore two di erent techniques to deal with unbalanced data were
explored, namely resampling and cost-sensitive learning.
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Faculteit der Sociale Wetenschappen