A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles
Nicolas Kardous, Amaury Hayat, Sean T. McQuade, Xiaoqian Gong, Sydney, Truong, Tinhinane Mezair, Paige Arnold, Ryan Delorenzo, Alexandre Bayen,, Benedetto Piccoli

TL;DR
This paper develops a rigorous multi-population traffic model incorporating autonomous and human-driven vehicles, demonstrating how small AV control inputs can dissipate traffic waves and improve flow stability through a mean-field limit approach.
Contribution
It introduces a novel multi-lane multi-population microscopic traffic model with a mean-field limit, enabling analysis of autonomous vehicle control effects on traffic wave dissipation.
Findings
Small percentage of AVs can effectively smooth traffic flow
Control strategies are robust across parameter variations
Mean-field limit yields coupled PDE-ODE system for large populations
Abstract
In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5\% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on three components that we treat as parameters in the model: safety, incentive and cool-down time. The choice of these parameters in the lane-change mechanism is critical to modeling traffic accurately, because different parameter values can lead to drastically different traffic behaviors. In particular, the number of lane-changes and the…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
