On characterizing the relationship between route choice behavior and optimal traffic control solution space

Explicitly including the dynamics of users' route choice behaviour in optimal traffic control applications has been of interest for researchers in the last five decades. This has been recognized as a very challenging problem, due to the added layer of complexity and the considerable non-convexity of the resulting problem, even when dealing with simple static assignment and analytical link cost functions. In this work we establish a direct behavioural connection between the different shapes and structures emerging in the solution space of such problems and the underlying route choice behaviour. We specifically investigate how changes in the active equilibrium route set exert direct influence on the solution space's structure and behaviour. Based on this result, we then formulate and validate a constrained version of the original problem, yielding desirable properties in terms of solution space regularity. © 2017 The Authors

An extended coordinate descent method for distributed anticipatory network traffic control

Anticipatory optimal network control can be defined as the practice of determining the set of control actions that minimizes a network-wide objective function, so that the consequences of this action are taken in consideration not only locally, on the propagation of flows, but globally, taking into account the user's routing behavior. Such an objective function is, in general, defined and optimized in a centralized setting, as knowledge regarding the whole network is needed in order to correctly compute it. This is a strong theoretical framework but, in practice, reaching a level of centralization sufficient to achieve said optimality is very challenging. Furthermore, even if centralization was possible, it would exhibit several shortcomings, with concerns such as computational speed (centralized optimization of a huge control set with a highly nonlinear objective function), reliability and communication overhead arising.The main aim of this work is to develop a decomposed heuristic descent algorithm that, demanding the different control entities to share the same information set, attains network-wide optimality through separate control actions. © 2015 Elsevier Ltd

A sensitivity-based approach for adaptive decomposition of anticipatory network traffic control

Anticipatory optimal network control is defined as the problem of determining the set of control actions that minimizes a network-wide objective function. This not only takes into account local consequences on the propagation of flows, but also the global network-wide routing behavior of the users. Such an objective function is, in general, defined in a centralized setting, as knowledge regarding the whole network is needed to correctly compute it. Reaching a level of centralization sufficient to attain network-wide control objectives is however rarely realistic in practice. Multiple authorities are influencing different portions the network, separated either hierarchically or geographically. The distributed nature of networks and traffic directly influences the complexity of the anticipatory control problem. This is our motivation for this work, in which we introduce a decomposition mechanism for the global anticipatory network traffic control problem, based on dynamic clustering of traffic controllers. Rather than solving the full centralized problem, or blindly performing a full controller-wise decomposition, this technique allows recognizing when and which controllers should be grouped in clusters, and when, instead, these can be optimized separately. The practical relevance with respect to our motivation is that our approach allows identification of those network traffic conditions in which multiple actors need to actively coordinate their actions, or when unilateral action suffices for still approximating global optimality. This clustering procedure is based on well-known algebraic and statistical tools that exploit the network's sensitivity to control and its structure to deduce coupling behavior. We devise several case studies in order to assess our newly introduced procedure's performances, in comparison with fully decomposed and fully centralized anticipatory optimal network control, and show that our approach is able to outperform both centralized and decomposed procedures. © 2016 Elsevier Ltd