Autonomic systems design for ITS applications

Kotsialos, Apostolos; Poole, Adam

doi:10.1109/itsc.2013.6728230

“…systems that exhibit self- * (e.g. self-optimising, self-healing, self-configuring and so forth) properties, the model validation problem's scope was extended to automate elements that traditionally are based on engineering expert opinion [26]. This is one of this paper's contributions.…”

Section: Introductionmentioning

confidence: 99%

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

Poole

¹

,

Kotsialos

²

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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“…The motivation behind this is that current calibration practice either uses expert engineering opinion to make a decision about the FD or use a separate FD for every discrete road segment resulting from the model's discretisation rules. In the first case, intuition, past experience, visual inspection and preliminary data analysis result to an ad-hoc approach leading away from systems that embed knowledge in their own structure and the display of more intelligent forms of automation, Kotsialos and Poole (2013). In the latter case, overparametrisation is a clear risk since typically three parameters are necessary for defining a FD.…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Model Validation Using METANET, ADOL-C and RPROP

Poole

¹

,

Kotsialos

²

2016

IFAC-PapersOnLine

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3

0

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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-prot 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. Abstract: Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using automatic differentiation. The ADOL-C library is coupled with the METANET source code and this system is embedded within an optimisation algorithm based on RPROP. The result is a very efficient system which is able to be calibrate METANET's second order model by determining the density and speed equation parameters as well as the fundamental diagrams used. Information obtained from the system's Jacobian provides extra insight into the system dynamics. A 22 km site is considered near Sheffield, UK and the results of a typical calibration and validation process are reported.

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