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See detailA sensitivity-based approach for adaptive decomposition of anticipatory network traffic control
Rinaldi, Marco UL; Himpe, W.; Tampère, C. M. J.

in Transportation Research. Part C : Emerging Technologies (2016), 66

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 ... [more ▼]

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. [less ▲]

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See detailRepeated anticipatory network traffic control using iterative optimization accounting for model bias correction
Huang, Wei; Viti, Francesco UL; Tampere, Chris

in Transportation Research. Part C : Emerging Technologies (2016)

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See detailImproving the efficiency of repeated dynamic network loading through marginal simulation
Corthout, Ruben; Himpe, Willem; Viti, Francesco UL et al

in Transportation Research. Part C : Emerging Technologies (2014), 41

Currently, the applicability of macroscopic Dynamic Network Loading (DNL) models for large-scale problems such as network-wide traffic management, reliability and vulnerability studies, network design ... [more ▼]

Currently, the applicability of macroscopic Dynamic Network Loading (DNL) models for large-scale problems such as network-wide traffic management, reliability and vulnerability studies, network design, traffic flow optimization and dynamic origin–destination (OD) estimation is computationally problematic. The main reason is that these applications require a large number of DNL runs to be performed. Marginal DNL simulation, introduced in this paper, exploits the fact that the successive simulations often exhibit a large overlap. Through marginal simulation, repeated DNL simulations can be performed much faster by approximating each simulation as a variation to a base scenario. Thus, repetition of identical calculations is largely avoided. The marginal DNL algorithm that is presented, the Marginal Computation (MaC) algorithm, is based on first order kinematic wave theory. Hence, it realistically captures congestion dynamics. MaC can simulate both demand and supply variations, making it useful for a wide range of DNL applications. Case studies on different types of networks are presented to illustrate its performance. [less ▲]

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See detailCalibration of a microscopic simulation model for emission calculation
Li, Jie; Van Zuylen, Henk J.; Chen, Yusen et al

in Transportation Research. Part C : Emerging Technologies (2013), 31

Emissions by road traffic can be reduced by optimising traffic control. The impact of this optimisation on emission can be analysed ex ante by simulation. The simulation programs used for this analysis ... [more ▼]

Emissions by road traffic can be reduced by optimising traffic control. The impact of this optimisation on emission can be analysed ex ante by simulation. The simulation programs used for this analysis should be valid with respect to the traffic characteristics that determine the emissions. Thus calibration of the parameters is a prerequisite. In most cases, volumes, travel times and queues are used to calibrate simulation models, rather than detailed driving characteristics such as speed and acceleration patterns. However, these driving behaviour parameters determine the vehicular emissions to a great extent. A study was carried out in which the driving behaviour parameters in a microscopic simulation model (VISSIM) were calibrated using real trajectories collected by image processing at an intersection in Rotterdam. The sensitivity of the simulation results for driving behaviour parameters was investigated. The most influential parameters were identified and adjusted to ensure that the simulation results were consistent with the observed traffic and could provide valid estimations of the total production of emissions. [less ▲]

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