  This article presents a model for traffic incident prediction. Specifically,
we address the fundamental problem of data scarcity in road traffic accident
prediction by training our model on emergency braking events instead of
accidents. Based on relevant risk factors for traffic accidents and
corresponding data categories, we evaluate different options for preprocessing
sparse data and different Machine Learning models. Furthermore, we present a
prototype implementing a traffic incident prediction model for Germany based on
emergency braking data from Mercedes-Benz vehicles as well as weather, traffic
and road data, respectively. After model evaluation and optimisation, we found
that a Random Forest model trained on artificially balanced (under-sampled)
data provided the highest classification accuracy of 85% on the original
imbalanced data. Finally, we present our conclusions and discuss further work;
from gathering more data over a longer period of time to build stronger
classification systems, to addition of internal factors such as the driver's
visual and cognitive attention.

Machine Learning;Computers and Society