Calibration of Microscopic Traffic Simulation Models

Abstract

A mathematical framework and a solution approach are presented for the simultaneous calibration of the demand and supply parameters and inputs to microscopic traffic simulation models as well as a large-scale application emphasizing practical issues. Microscopic traffic simulation models provide detailed estimates of evolving network conditions by modeling time-varying demand patterns and individual drivers' detailed behavioral decisions. Such models are composed of elements that simulate different demand and supply processes and their complex interactions. Several model inputs (such as origin-destination flows) and parameters (car-following and lane-changing coefficients) must be specified before these simulation tools can be applied, and their values must be determined so that the simulation output accurately replicates the reality reflected in traffic measurements. A methodology is presented here for simultaneously estimating all microscopic simulation model parameters by using general traffic measurements. A large-scale case study for the calibration of the MITSimLab microscopic traffic simulation model by using the network of Lower Westchester County, New York, is employed to demonstrate the feasibility, application, and benefits of the proposed methodology.