Recent work has shown that an innate instability exists on urban traffic networks that causes congestion to tend toward inhomogeneous spatial distributions. This can lead to less consistent and reproducible relationships between average flow and density across a network (known commonly as the Macroscopic Fundamental Diagram or MFD). Since well-defined MFDs show promise in the design and control of urban networks, it is important to find ways to avoid this unstable behavior. The present paper explores how well traffic signals with adaptive green durations might be able to mitigate this instability and provide more consistent MFDs. A family of adaptive signal control strategies is examined on two abstractions of an idealized grid network using an interactive simulation and analytical model. The results suggest that adaptive traffic signals should provide a stabilizing influence, which increases average flows and decreases the likelihood of gridlock, when the network is moderately congested. The benefits in moderately congested states increase with the adaptivity of the signals. However, when the network is extremely congested, vehicle movements become more constrained by downstream congestion and queue spillbacks than by traffic signals, and adaptive traffic signals appear to have little to no effect on the network or MFD. In this latter case, other strategies should be used to mitigate the instability, like adaptively routing drivers to avoid locally congested regions. These results are consistent with simulations of more realistic adaptive signal control schemes that have been studied in the literature.
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