  Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion,
but detection is not enough because congestion can be further classified as
recurrent and non-recurrent congestion (NRC). In particular, NRC in an urban
network is mainly caused by incidents, workzones, special events and adverse
weather. We propose a framework for the real-time distributed classification of
congestion into its components on a heterogeneous urban road network using
VANET. We present models built on an understanding of the spatial and temporal
causality measures and trained on synthetic data extended from a real case
study of Cologne. Our performance evaluation shows a predictive accuracy of
87.63\% for the deterministic Classification Tree (CT), 88.83\% for the Naive
Bayesian classifier (NB), 89.51\% for Random Forest (RF) and 89.17\% for the
boosting technique. This framework can assist transportation agencies in
reducing urban congestion by developing effective congestion mitigation
strategies knowing the root causes of congestion.
