Improved Calibration Method for Dynamic Traffic Assignment Models
Name
AnImprovedCalibrationMethodforDynamicTrafficAssignmentModels_ConstrainedExtendedKalmanFilter.pdf
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1.9 MB
Format
Adobe PDF
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Author(s)
Seshadri, Ravi
Prakash, A. Arun
Pereira, Francisco C.
Antoniou, Constantinos
Zhang, Haizheng
Ben-Akiva, Moshe E
Date Issued
2017
Journal
Transportation Research Record: Journal of the Transportation Research Board
Publisher
SAGE Publications
Citation
Zhang, Haizheng, et al. “Improved Calibration Method for Dynamic Traffic Assignment Models.” Transportation Research Record: Journal of the Transportation Research Board 2667 (January 2017): 142–153
Version
Author's final manuscript
Abstract
The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.3141/2667-14
