Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models

Constantinos Antoniou · Moshe E Ben-Akiva · Haris N. Koutsopoulos

186 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

Abstract

An online calibration approach that jointly estimates demand and supply parameters of dynamic traffic assignment (DTA) systems is presented and empirically validated through an extensive application. The problem can be formulated as a nonlinear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter, and therefore, nonlinear extensions need to be considered. The following three extensions to the Kalman filtering algorithm are presented: 1) the extended Kalman filter (EKF); 2) the limiting EKF (LimEKF); and 3) the unscented Kalman filter. The solution algorithms are applied to the on-line calibration of the state-of-the-art DynaMIT DTA model, and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy that is comparable to that of thebestalgorithm but with vastly superior computational performance. The robustness of the approach to varying weather conditions is demonstrated, and practical aspects are discussed.

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1991Dynamic network models and driver information systems 2002Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows 2004An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables 1996Estimation and prediction of time-dependent origin-destination flows 2004Incorporating Automated Vehicle Identification Data into Origin-Destination Estimation 1997Demand Simulation for Dynamic Traffic Assignment 2005Calibration and Validation of Dynamic Traffic Assignment Systems 2004On-line calibration for dynamic traffic assignment

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