Queue Spillovers in Transportation Networks with a Route Choice

This paper explores some of the traffic phenomena that arise when drivers have to navigate a network in which queues back up past diverge intersections. If a diverge provides two alternative routes to the same destination and the shorter route has a bottleneck that generates a queue, one would expect that queue to stabilize at an equilibrium level where the travel time on both routes is roughly equal. If the capacity of the longer route is unlimited then this network can accommodate any demand level. However, if the bottleneck is so close to the upstream end of the link that the equilibrium queue cannot be contained in the link, then the trip time on the queued route cannot grow to match that on the alternate route. This means that the alternative route can never be attractive, even if the queue spills back past the diverge, and that drivers approaching the diverge will act as if the alternative route did not exist. As a result, a steady flow into the system greater than the capacity of the bottleneck will cause a queue to grow all the way back to the origin (blocking it). The final result is an Òoversaturated static stateÓ where the queue regulates the input flow into the system. Curiously, if the bottleneck capacity of this network is reduced below a critical level (or is eliminated altogether) then the alternative route becomes attractive again and the system cannot reach the saturation point. This phenomenon is explored in the paper, where it is also shown that: (i) a network can become permanently oversaturated/undersaturated as a result of a temporary increase/decrease in link capacity, (ii) even under the most favorable assumptions, and in contrast to the equivalent assignment problem with point queues, a network can be stable both in an oversaturated and an undersaturated state, and (iii) temporary endogenous disturbances can permanently reverse the saturation state of a network. These findings suggest that the time-dependent traffic assignment problem with physical queues is chaotic in nature and that (as in weather forecasting) it may be impossible to obtain input data with the required accuracy to make reliable predictions of cumulative output flows on severely congested networks.