Distributed Classification of Urban Congestion Using VANET

TL;DR

This paper introduces a real-time distributed framework using VANETs to classify urban traffic congestion into recurrent and non-recurrent types, aiding targeted mitigation strategies.

Contribution

It presents a novel VANET-based classification framework with models trained on synthetic data, achieving high accuracy in distinguishing congestion causes.

Findings

Achieved up to 89.51% accuracy with Random Forest.

Effective real-time classification of congestion types.

Framework aids targeted congestion mitigation strategies.

Abstract

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.