Piecewise-Linearized Cell Transmission Model and Parameter Calibration Methodology

Muñoz, Laura; Sun, Xiaotian; Horowitz, Roberto; Alvarez, Luis

doi:10.3141/1965-19

Cited by 53 publications

(45 citation statements)

References 17 publications

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“…The trapezoidal CTM has found various applications, e.g., in freeway ramp metering, signal optimization, and traffic state estimation (Friedrich and Almasri, 2006;Feldman and Maher, 2002;Sun et al, 2003;Tampere and Immers, 2007), and has been subject to thorough experimental validations (e.g., Brockfeld and Wagner, 2006;Munoz et al, 2006Munoz et al, , 2004. Early network implementations are NETCELL (Cayford et al, 1997), and the model of Lee (1996); Ziliaskopoulos and Lee (1997).…”

Section: Synthetic Experimentsmentioning

confidence: 99%

Operational macroscopic modeling of complex urban road intersections

Flötteröd

Rohde

2011

Transportation Research Part B: Methodological

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This article describes a new approach to the macroscopic first order modeling and simulation of traffic flow in complex urban road intersections. The framework is theoretically sound, operational, and comprises a large body of models presented so far in the literature.Working within the generic node model class of Tampere et al. (forthcoming), the approach is developed in two steps. First, building on the incremental transfer principle of Daganzo et al. (1997), an incremental node model for general road intersections is developed. A limitation of this model (as of the original incremental transfer principle) is that it does not capture situations where the increase of one flow decreases another flow, e.g., due to conflicts. In a second step, the new model is therefore supplemented with the capability to describe such situations. A fixed-point formulation of the enhanced model is given, solution existence and uniqueness are investigated, and two solution algorithms are developed. The feasibility and realism of the new approach is demonstrated through both a synthetic and a real case study.

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Section: Synthetic Experimentsmentioning

confidence: 99%

Operational macroscopic modeling of complex urban road intersections

Flötteröd

Rohde

2011

Transportation Research Part B: Methodological

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“…This extra requirement increases the problem complexity but removes the need for prior expert knowledge about congestion patterns. Bottleneck identification as used in [27,28] or choosing an arbitrary (based on educated opinion) point for a change in the FD parameter set [16] is not explicitly required. These are left to the optimisation algorithm to deal with, aiming at avoiding overparametrisation as well.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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“…In the SMM [14], (3) is written as a hybrid linear system whose system dynamics switches among different modes depending on the state of the boundary cells.…”

Section: B Switching Mode Modelmentioning

confidence: 99%

Scaling the Kalman Filter for Large-Scale Traffic Estimation

Sun

Work

2018

IEEE Trans. Control Netw. Syst.

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This work introduces a scalable filtering algorithm for multi-agent traffic estimation. Large-scale networks are spatially partitioned into overlapping road sections. The traffic dynamics of each section is given by the switching mode model (SMM) using a conservation principle, and the traffic state in each section is estimated by a local agent.In the proposed filter, a consensus term is applied to promote inter-agent agreement on overlapping sections. The new filter, termed a (spatially) distributed local Kalman consensus filter (DLKCF), is shown to maintain globally asymptotically stable (GAS) mean error dynamics when all sections switch among observable modes. When a section is unobservable, we show that the mean estimate of each state variable in the section is ultimately bounded, which is achieved by exploring the interaction between the properties of the traffic model and the measurement feedback of the filter. Based on the above results, the boundedness of the mean estimation error of the DLKCF under switching sequences with observable and unobservable modes is established to address the overall performance of the filter.Numerical experiments show the ability of the DLKCF to promote consensus, increase estimation accuracy compared to a local filter, and reduce the computational load compared to a centralized approach.

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Piecewise-Linearized Cell Transmission Model and Parameter Calibration Methodology

Cited by 53 publications

References 17 publications

Operational macroscopic modeling of complex urban road intersections

Operational macroscopic modeling of complex urban road intersections

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Scaling the Kalman Filter for Large-Scale Traffic Estimation

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