Network packet

Random forest

Engineering

Transport engineering

Bayesian network

Support vector machine

Network congestion

Wireless ad hoc network

Telecommunications

Computer science

Computer network

Boosting (machine learning)

Naive Bayes classifier

Artificial intelligence

Vehicular ad hoc network

Wireless

Traffic congestion

Intelligent transportation system

IEEE Transactions on Intelligent Transportation Systems

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.

Distributed Classification of Urban Congestion Using VANET