Empirical Study of Traffic Velocity Distribution and its Effect on VANETs Connectivity

TL;DR

This paper uses real traffic data to analyze vehicle velocity distributions, confirming Gaussian models in steady states and GEV models during transitions, and examines how these affect connectivity in VANETs.

Contribution

It provides empirical evidence for velocity distribution models in different traffic regimes and assesses their impact on VANET connectivity estimation.

Findings

Vehicle velocities follow Gaussian distribution in steady traffic.

Transition phases are better modeled by generalized extreme value distribution.

Velocity models significantly influence connectivity duration estimates.

Abstract

In this article we use real traffic data to confirm that vehicle velocities follow Gaussian distribution in steady state traffic regimes (free-flow, and congestion). We also show that in the transition between free-flow and congestion, the velocity distribution is better modeled by generalized extreme value distribution (GEV). We study the effect of the different models on estimating the probability distribution of connectivity duration between vehicles in vehicular ad-hoc networks.