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