Quasi-dynamic traffic assignment with spatial queueing, control and blocking back

in Transportation Research. Part B, Methodological (2019)

This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage ... [more ▼]

This paper introduces a steady-state, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queueing delays and explicit bounds on queue storage capacities. The model is a quasi-dynamic model. The link model at the heart of this quasi-dynamic equilibrium model is a spatial queueing model, which takes account of the space taken up by queues both when there is no blocking back and also when there is blocking back. The paper shows that if this quasi-dynamic model is utilised then for any feasible demand there is an equilibrium solution, provided (i) queue storage capacities are large or (ii) prices are used to help impose capacity restrictions; the prices either remove queueing delays entirely or just reduce spatial queues sufficiently to ensure that blocking back does not occur at equilibrium. Similar results, but now involving the P0 control policy (introduced in Smith (1979a, 1987)) and two new variations of this policy (i.e., the spatial P0 control policy, and the biased spatial P0 control policy) are obtained. In these results, the control policies allow green-times to vary in response to prices as well as spatial queueing delays. These three policies are also tested on a small simple network. In these tests, the biased spatial version of P0 is much the best in reducing equilibrium delays (on this simple network). The paper further illustrates how the spatial queueing model works on simple networks with different merge models; it is demonstrated that equilibrium may be prevented by certain (fixed ratio) merge models. It is also shown in this case that equilibrium may be imposed on just the controlled area itself by a variety of (merge model, gating strategy) combinations. Opportunities for developing such combined gating and merging control strategies are finally discussed. [less ▲]

A dynamic route swapping and control algorithm that maximises network capacity accounting for node constraints and blocking back

Scientific Conference (2016, July)

A dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity

in Journal of Advanced Transportation (2016)

An Optimization-Based Iterative Learning Method for Anticipatory Network Traffic Control

in Proceedings of the 16th COTA International Conference of Transportation Professionals (2016)

An integrated approach to adaptive anticipatory traffic control and parameter estimation

in Proceedings of the MT-ITS Conference (2015, June)

An adaptive flow anticipatory control using daily iterative learning in urban traffic networks

Scientific Conference (2014, June)

Equilibrium in capacitated network models with queueing delays, queue-storage, blocking back and control

in Procedia Social and Behavioral Sciences (2013), 80

This paper considers a steady-state, link-based, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queuing delays and explicit bounds on queue ... [more ▼]

This paper considers a steady-state, link-based, fixed (or inelastic) demand equilibrium model with explicit link-exit capacities, explicit bottleneck or queuing delays and explicit bounds on queue storage capacities. The (spatial queueing) model at the heart of this equilibrium model takes account of the space taken up by queues both when there is no blocking back and also when there is blocking back. The paper shows in theorem 1 that a feasible traffic assignment model has an equilibrium solution provided prices are used to impose capacity restrictions and utilises this result to show that there is an equilibrium with the spatial queueing model, provided queue-storage capacities are sufficiently large. Other results are obtained by changing the variables and sets in theorem 1 suitably. These results include: (1) existence of equilibrium results (in both a steady state and a dynamic context) which allow signal green-times to respond to prices and (2) an existence of equilibrium result which allow signal green-times to respond to spatial queues; provided this response follows the P0 control policy in Smith (1979, 1987). These results show that under certain conditions the P0 control policy maximises network capacity. The spatial queueing model is illustrated on a simple network. Finally the paper includes elastic demand; this is necessary for long-run evaluations. Each of the steady state models here may be thought of as a stationary solution to the dynamic assignment problem either with or without blocking back. [less ▲]

Combining demand management and merge control in an equilibrium network model

Scientific Conference (2013)

Equilibrium models under congested traffic conditions, and especially those addressing blocking back, are very useful to estimate the demand conditions that ITS policies should be able to manage, for ... [more ▼]

Equilibrium models under congested traffic conditions, and especially those addressing blocking back, are very useful to estimate the demand conditions that ITS policies should be able to manage, for instance to maintain congestion within controlled areas and avoiding that they further spillback and cause more serious and/or less controllable congestion states. The objective of this paper is to supplement the equilibrium model, developed by the authors in recent research, with a more thorough analysis of merge behaviour, especially in cases of blocked nodes. Regulating the merger behaviour together with the demand pattern can lead to certain desired stationary states. It has a great practical significance when congestion is inevitable, while demand management and merge control are able to retain queues and spill-backs within the local area. [less ▲]