METANET Validation of the Large-Scale Manchester Ring-Road Network Using Gradient-Based and Particle Swarm Optimization

Abstract

This paper is concerned with the problem of macroscopic traffic flow model validation for ring-road shaped large-scale motorway networks. The calibration optimization problem is solved by a gradient-based algorithm combining into a single software package METANET (traffic simulator), RPROP (resilient backpropagation search heuristic), and ADOL-C (automatic differentiation library) and by a separate implementation of particle swarm optimization with METANET. These model validation packages are applied to the motorway network around the city of Manchester, U.K. The total road length of the site is 186 km considering the traffic flow on both directions of each modeled motorway. For this large scale network, a single optimization problem is formed for calibrating METANET, i.e., for identifying its parameters. Three different data sets are used and the corresponding optimal parameter sets are obtained. The results show that the combined METANET–RPROP–ADOL-C package is able to calibrate the large scale motorway network model with very good accuracy. The optimal parameter sets, where the optimization algorithm converged for a particular data set, are verified by running METANET simulations using the other two data sets. Results do show the expected degradation of the parameter set’s quality, but the essential network wide dynamics of congestion are retained.