Network capacity, traffic instability, and adaptive driving: findings from simulated urban network experiments

This paper provides a discussion of network traffic flow stability and network capacity including existing definitions and measurement methods in the literature. To characterize network traffic flow, three phases are defined as free flow (F), inhomogenously congested (IC), and homogenously congested. It is shown that in large non-homogenous networks, the F → IC transition is usually a hysteretic phase transition. This paper examines the effects of adaptive driving on network capacity and traffic instability in a simulated network model of the Chicago metropolitan area as a large-scale highly congested complex network with exit flows. Results show that when the number of adaptive drivers increases, the general trend for network capacity is also increasing with some fluctuations due to randomness of traffic and overlapping effects of route choices. It is also found that adaptive driving may increase average network flow but may not necessarily improve network output, especially when the population of adaptive drivers is large and no gridlock exists. Results also suggest that with a larger population of adaptive drivers, hysteresis and gridlock are less likely to form. However, it is shown that bifurcation and multivaluedness can still occur even when the entire population of drivers is adaptive.