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

Abstract

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.

Second order macroscopic traffic flow model validation using automatic differentiation with resilient backpropagation and particle swarm optimisation algorithms

“…A more detailed calibration work using classic and recent variants of particle swarm optimisation (PSO) and cuckoo search algorithms is reported by Poole and Kotsialos (2016). These evolutionary algorithms (EA) were used for calibrating a site near Heathrow Airport and a site near Sheffield, UK.…”

“…Optimal parameters were determined capturing the essential characteristics of the underlying traffic dynamics as was shown in the ensuing model verification. In total, ten different EA, seven variations of particle swarm optimisation (PSO), two of cuckoo search and a simple genetic algorithm acting as a baseline, were considered by Poole and Kotsialos (2016). One of the main conclusions drawn regarding algorithmic performance, was that the PSO family of algorithms outperformed the cuckoo search and genetic algorithm; interestingly it was one of the simplest types of PSO, a variant called Local PSO (LPSO) (Kennedy and Mendes, 2002), that proved to be the more efficient among the PSO variants, in terms of number of iterations and error.…”

Traffic Flow Forecasting for Road Tunnel Using PSO-GPR Algorithm with Combined Kernel Function

“…These memories are used to adjust the particles' own velocities and their subsequent positions. The position and velocity of the particles can be determined and updated using the following equation [26]:…”

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

“…A genetic algorithm was used there in order to demonstrate the soundness of the approach. Based on this work, a more detailed calibration work using classic and recent variants of particle swarm optimisation (PSO) and cuckoo search algorithms was reported in Poole and Kotsialos (2016). These evolutionary algorithms were used for calibrating the Heathrow site used in Poole and Kotsialos (2012) in addition to a road stretch near Sheffield, which is considered here as well.…”

“…The results reported demonstrate the validity of the approach. Optimal parameters were determined capturing the essential characteristics of the underlying traffic dynamics as was shown in the ensuing model validation, see Poole and Kotsialos (2016) for more details.…”