On the impacts of locally adaptive signal control on urban network stability and the Macroscopic Fundamental Diagram

• Investigates and compares methods to mitigate instabilities on congested networks.
• Analytical models used to obtain insights.
• Models validated with idealized and more realistic simulations.
• Adaptive signal control shown to be beneficial for moderate congestion levels.
• Adaptive driver routing more robust than adaptive signal control and is also beneficial in heavy congestion.

Urban traffic networks are inherently unstable when congested. This instability causes a natural tendency towards spatially inhomogeneous vehicle distributions and less consistent and reproducible relationships between urban traffic variables. It is important to find ways to mitigate this unstable behavior since well-defined relationships between average network flow and density – the MFD – are useful to aid network design and control.This paper examines the impacts of locally adaptive traffic signals – e.g., those that allocate green times proportionally to upstream approach densities – on network stability and the MFD. A family of adaptive signal control strategies is examined on two abstractions of an idealized grid network using an analytical model and an interactive simulation. The results suggest that locally adaptive traffic signals provide stability when the network is moderately congested, which increases average flows and decreases the likelihood of gridlock. These benefits increase with the overall adaptivity of the signals. However, adaptive signals appear to have little to no effect on network stability or the MFD in heavily congested networks as vehicle movement becomes more constrained by downstream congestion and queue spillbacks. Under these conditions, other strategies should be used to mitigate the instability, such as adaptively routing drivers to avoid locally congested regions. These behaviors are verified using more realistic micro-simulations and are consistent with other observations documented in the literature.