Efficient Traffic State Estimation and Prediction Based on the Ensemble Kalman Filter with a Fast Implementation and Localized Deterministic Scheme | IEEE Conference Publication | IEEE Xplore

Efficient Traffic State Estimation and Prediction Based on the Ensemble Kalman Filter with a Fast Implementation and Localized Deterministic Scheme


Abstract:

Traffic state estimation and forecasting are central components in dynamic traffic management and information applications. This paper proposes a traffic state estimation...Show More

Abstract:

Traffic state estimation and forecasting are central components in dynamic traffic management and information applications. This paper proposes a traffic state estimation approach based on an improved formulation of the traditional Ensemble Kalman filter (EnKF), including a fast implementation and a localized deterministic scheme. A reformulation of the EnKF equations leads to efficient computation. The deterministic scheme implies that we use the same observations for each of the ensembles instead of randomized observations. The use of a deterministic algorithm can reduce the impact of coincidental sampling and associated sampling errors. Localization is in contrast with a global method. In the global method, both the evolution of system states and the incorporation of observations are considered as an entity (within a global matrix). Here, the inclusion of localization has several potential advantages for large-scale applications: blocking spurious correlations, decreasing computation time due to smaller matrix inversions, increasing the accuracy by increasing the effective ensemble size. The proposed implementation of the EnKF for traffic state estimation and prediction is tested and validated in a realistic Dutch freeway network. The experiment studies deliver promising results for large-scale practical applications.
Date of Conference: 15-18 September 2015
Date Added to IEEE Xplore: 02 November 2015
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Conference Location: Gran Canaria, Spain

I. Introduction

Traffic state estimation (TSE) and forecasting are central components in dynamic traffic management and information applications. Generally model-based TSE relies on two components: a model-based component and a data assimilation algorithm. The model-based component consists of two parts: a) a dynamic traffic flow model to predict the evolution of the state variables; and b) a set of observation equations relating sensor observations to the system state. Thereafter, a data-assimilation technique is adopted to combine the model predictions with the sensor observations.

References

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