Calibration of Microscopic Traffic Simulation Models with Aggregate Data

Abstract

A framework for the calibration of microscopic traffic simulation models using aggregate data is presented. The framework takes into account the interactions between the various inputs and parameters of the simulator by estimating origin-destination (O-D) flows jointly with the behavioral parameters. An optimization-based approach is used for the joint calibration. Since the calibration of the parameters depends on the estimated O-D flows and vice versa, the proposed framework is iterative. O-D estimation is based on the well-known generalized least squares estimator. A systematic search approach based on the complex algorithm is adopted for calibration of the behavioral parameters. This algorithm is particularly useful for the problem at hand since it does not require calculations of derivatives of the objective function. The applicability of the approach is demonstrated through its application to case studies using MITSIMLab, a microscopic traffic simulation model.

A case for real-time calibration of data-driven microscopic traffic simulation tools

“…Also, Oketch and Carrick presented a method that first determines suitable values for model parameters such as, aggressiveness, awareness, target headways and reaction times that provided realistic results and second estimates representative origindestination (OD) matrices (Oketch and Carrick 2005). Toledo et al employed an iterative approach that calibrated a model by jointly estimating OD flows and values for behavioral parameters (Toledo et al 2004). Efforts presented by (Dowling, Holland, and Huang 2002;Dowling, Skabardonis, and Alexiadis 2004;) illustrated a series of iterations to a method whose foundation employed a four step process to calibrate a model.…”

A new approach to calibrating time-dependent origin-destination departure profile for traffic simulation models

“…The calibration efforts can be generally categorized, but not limited, into several dimensions, such as: [2], He, R and B. Ran [3], Lianyu, C., H. X. Liu, et al [4], and Toledo, T., M. Ben-Akiva, et al [5].…”

Multi-criteria analysis of optimal signal plans using microscopic traffic models

“…However, it is, in practice, more convenient and feasible to calibrate models using aggregate traffic flow data (e.g. Toledo et al, 2004). Therefore, driver behavior parameters in VISSIM are first adjusted using collected traffic data in this study.…”