Holistic Calibration of Microscopic Traffic Flow Models: Methodology and Real World Application Studies

This study proposes and applies a methodology to calibrate microscopic traffic flow simulation models. The proposed methodology has the capability to calibrate simultaneously all the calibration parameters as well as demand patterns for any type of network. Parameters considered include global and local as well as driver behaviour and vehicle performance parameters. Demand patterns, in terms of turning volumes, are included in the calibration framework. Multiple performance measures involving link counts and speeds are used to formulate and solve the proposed calibration problem. In addition, multiple time periods were considered. A Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used to search for the vector of the model’s parameters that minimizes the difference between actual and simulated network states. Punzo et al. [Punzo et al(2012)Punzo, Ciuffo, and Montanino] commented on the uncertainties present in many calibration methodologies. The motivation to consider simultaneously all model parameters is to reduce that uncertainties to a minimum, by leaving to the experience of the engineers as little parameter tuning as possible. The effects of changing the values of the parameters are taken into consideration to adjust them slightly and simultaneously. This results in a small number of evaluations of the objective function. Three networks were calibrated with excellent results. The first network was an arterial network with link counts and speeds used as performance measurements for calibration. The second network included a combination of freeway ramps and arterials, with link counts used as performance measurements. The Alexander Paz, Ph.D., P.E. Department of Civil and Environmental Engineering University of Nevada, Las Vegas, USA. email: [email protected] Victor Molano Department of Civil and Environmental Engineering University of Nevada, Las Vegas, USA. email: [email protected] Javier Sanchez-Medina CICEI, Computer Science Department, University of Las Palmas de Gran Canaria (ULPGC), Spain. e-mail: [email protected]