Proceedings ArticleDOI
A comparison of filter configurations for freeway traffic state estimation
Andreas Hegyi,D. Girimonte,Robert Babuska,B. De Schutter +3 more
- pp 1029-1034
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TLDR
The main conclusions from the simulations are that the performance of the extended Kalman filter and the unscented Kalmanfilter is comparable, joint filtering performs significantly better than dual filtering, and a larger number of detectors results in better state estimation, but has no significant influence on the parameter estimation error.Abstract:
We present a comparison for several filter configurations for freeway traffic state estimation. Since the environmental conditions on a freeway may change over time (e.g., changing weather conditions), parameter estimation is also considered. We compare the performance of the extended Kalman filter and the unscented Kalman filter for state estimation, parameter estimation, joint estimation and dual estimation. Furthermore, the performance is evaluated for different detector configurations. The main conclusions from the simulations are that (1) the performance of the extended Kalman filter and the unscented Kalman filter is comparable, (2) joint filtering performs significantly better than dual filtering, and (3) a larger number of detectors results in better state estimation, but has no significant influence on the parameter estimation errorread more
Citations
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Journal ArticleDOI
Traffic state estimation on highway: A comprehensive survey
TL;DR: A survey of highway TSE methods is conducted, and the recent usage of detailed disaggregated mobile data for the purpose of TSE is summarized, showing two possibilities in order to solve this problem: improvement of theoretical models and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider.
Journal ArticleDOI
Brief paper: Freeway traffic estimation within particle filtering framework
TL;DR: The problem of real-time estimation of traffic state in freeway networks by means of the particle filtering framework, developed based on a recently proposed speed-extended cell-transmission model of freeway traffic, is formulates.
Journal ArticleDOI
Real-Time Lagrangian Traffic State Estimator for Freeways
TL;DR: This paper proposes a new model-based state estimator based on the EKF technique, in which the discretized Lagrangian Lighthill-Whitham and Richards (LWR) model is used as the process equation, and in which observation models for both Eulerian andlagrangian sensor data are incorporated.
Journal ArticleDOI
An Approach to Urban Traffic State Estimation by Fusing Multisource Information
TL;DR: Tests on the accuracy, conflict resistance, robustness, and operation speed by real-world traffic data illustrate that the proposed information-fusion-based approach to the estimation of urban traffic states can well be used in urban traffic applications on a large scale.
Journal ArticleDOI
An adaptive freeway traffic state estimator
Yibing Wang,Markos Papageorgiou,Albert Messmer,Pierluigi Coppola,Athina Tzimitsi,Agostino Nuzzolo +5 more
TL;DR: Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features, and the achieved testing results are quite satisfactory and promising for further work and field applications.
References
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Proceedings ArticleDOI
The unscented Kalman filter for nonlinear estimation
Eric A. Wan,R. van der Merwe +1 more
TL;DR: The unscented Kalman filter (UKF) as discussed by the authors was proposed by Julier and Uhlman (1997) for nonlinear control problems, including nonlinear system identification, training of neural networks, and dual estimation.
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Beyond the Kalman Filter: Particle Filters for Tracking Applications
TL;DR: Part I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering and tracking applications: tracking a ballistic object bearings-only tracking range- only tracking bistatic radar tracking targets through blind Doppler terrain aided tracking detection and tracking of stealthy targets group and extended object tracking.
Journal ArticleDOI
A new method for the nonlinear transformation of means and covariances in filters and estimators
TL;DR: A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
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Kalman Filtering and Neural Networks
TL;DR: This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.
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Kalman Filtering: Theory and Practice
TL;DR: This paper presents a meta-modelling framework for Matrix Refresher that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually refreshing the Matrix.
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