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Mitigation of stop-and-go traffic waves with intelligent vehicles at low market penetration rates

Irene Martínez

TL;DR

The paper investigates phantom stop‑and‑go waves and compares multiple (C)ACC strategies under low market penetration using a stochastic NSCF framework with diverse vehicle types. It shows that multi‑vehicle anticipation (MAV) can achieve damping comparable to full connectivity even at MPR as low as 1%, while partial connectivity requires higher MPR. Two quantitative metrics are defined to gauge oscillation growth, and Monte Carlo simulations reveal that MAVs and FCAVs provide substantial mitigation, with FCAVs often eliminating stop‑and‑go at low MPR. The findings underscore multi‑leader sensing as a practical, cost‑effective alternative to full V2I connectivity for stabilizing traffic, though limitations in noise modeling and vehicle placement warrant further study.

Abstract

Stop-and-go traffic patterns sometimes manifest on roadways without any discernible congestion triggers. Such a phenomenon has been observed on homogeneous ring roads without lane changes. With the development of vehicle technology and measurement sensors, multiple researchers have focused on studying the influence of automated vehicles on traffic. In particular, there is a focus on the design of string-stable adaptive cruise control (ACC) strategies to dampen stop-and-go waves. However, there is no systematic comparison among different strategies nor a quantitative analysis of the oscillation reduction at low market penetration rates (MPRs). This paper proposes a framework to evaluate the impact of low MPRs across multiple ACC strategies. Then, through Monte Carlo simulations, our findings indicate that multi-vehicle anticipation technology yields nearly equivalent benefits in mitigating stop-and-go patterns compared to full vehicular connectivity, even at a modest MPR of 1\%. In contrast, partial connectivity among vehicles only eliminates stop-and-go waves if the MPR is larger.

Mitigation of stop-and-go traffic waves with intelligent vehicles at low market penetration rates

TL;DR

The paper investigates phantom stop‑and‑go waves and compares multiple (C)ACC strategies under low market penetration using a stochastic NSCF framework with diverse vehicle types. It shows that multi‑vehicle anticipation (MAV) can achieve damping comparable to full connectivity even at MPR as low as 1%, while partial connectivity requires higher MPR. Two quantitative metrics are defined to gauge oscillation growth, and Monte Carlo simulations reveal that MAVs and FCAVs provide substantial mitigation, with FCAVs often eliminating stop‑and‑go at low MPR. The findings underscore multi‑leader sensing as a practical, cost‑effective alternative to full V2I connectivity for stabilizing traffic, though limitations in noise modeling and vehicle placement warrant further study.

Abstract

Stop-and-go traffic patterns sometimes manifest on roadways without any discernible congestion triggers. Such a phenomenon has been observed on homogeneous ring roads without lane changes. With the development of vehicle technology and measurement sensors, multiple researchers have focused on studying the influence of automated vehicles on traffic. In particular, there is a focus on the design of string-stable adaptive cruise control (ACC) strategies to dampen stop-and-go waves. However, there is no systematic comparison among different strategies nor a quantitative analysis of the oscillation reduction at low market penetration rates (MPRs). This paper proposes a framework to evaluate the impact of low MPRs across multiple ACC strategies. Then, through Monte Carlo simulations, our findings indicate that multi-vehicle anticipation technology yields nearly equivalent benefits in mitigating stop-and-go patterns compared to full vehicular connectivity, even at a modest MPR of 1\%. In contrast, partial connectivity among vehicles only eliminates stop-and-go waves if the MPR is larger.
Paper Structure (9 sections, 6 equations, 7 figures, 1 table)

This paper contains 9 sections, 6 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Stop-and-go formation with noise
  4. Assumptions on different intelligent vehicle types
  5. Metrics to study the oscillation propagation
  6. Numerical Results
  7. Oscillations propagation vehicle to vehicle
  8. Ring road: Propagation of oscillations over time
  9. Discussion and conclusions

Figures (7)

  • Figure 1: Trajectories of vehicles with an initial spacing of 22m, where he color indicates the speed [m/s].
  • Figure 2: Speed variation of each vehicle in a platoon with one intelligent vehicle. (a) In position #50, (b) average over 500 MCS with a random location of the intelligent vehicle.
  • Figure 3: Variation of speed of each vehicle in the platoon under different MPR. Average over 250 MCS.
  • Figure 4: Single simulation run on a ring road of $L=2.5$ km with MPR=2% of Automated Vehicles (AVs). (a) Trajectories (b) The speed evolution in m/s.
  • Figure 5: Single simulation run on a ring road of $L=2.5$ km with MPR=2% of Multi-anticipation automated vehicles (MAVs). (a) Trajectories (b) The speed evolution in m/s.
  • ...and 2 more figures