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Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

Herman Sutarto (UGent) , René Boel (UGent) and Endra Joelianto
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Organization
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DISC (Distributed Supervisory Control of Large Plants)
Abstract
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator.
Keywords
urban road traffic, EM ALGORITHM, stochastic hybrid model, particle filtering, Markov processes, expectation-maximisation algorithm, parameter estimation

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Citation

Please use this url to cite or link to this publication:

Chicago
Sutarto, Herman, René Boel, and Endra Joelianto. 2015. “Parameter Estimation for Stochastic Hybrid Model Applied to Urban Traffic Flow Estimation.” Ed. James Lam. Iet Control Theory and Applications 9 (11): 1683–1691.
APA
Sutarto, H., Boel, R., & Joelianto, E. (2015). Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation. (J. Lam, Ed.)IET CONTROL THEORY AND APPLICATIONS, 9(11), 1683–1691.
Vancouver
1.
Sutarto H, Boel R, Joelianto E. Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation. Lam J, editor. IET CONTROL THEORY AND APPLICATIONS. The Institution of Engineering and Technology; 2015;9(11):1683–91.
MLA
Sutarto, Herman, René Boel, and Endra Joelianto. “Parameter Estimation for Stochastic Hybrid Model Applied to Urban Traffic Flow Estimation.” Ed. James Lam. IET CONTROL THEORY AND APPLICATIONS 9.11 (2015): 1683–1691. Print.
@article{5955231,
  abstract     = {This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation -- free flowing, congested or faulty -- making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator.},
  author       = {Sutarto, Herman and Boel, Ren{\'e} and Joelianto, Endra},
  editor       = {Lam, James},
  issn         = {1751-8652},
  journal      = {IET CONTROL THEORY AND APPLICATIONS},
  language     = {eng},
  number       = {11},
  pages        = {1683--1691},
  publisher    = {The Institution of Engineering and Technology},
  title        = {Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation},
  url          = {http://dx.doi.org/10.1049/iet-cta.2014.0909},
  volume       = {9},
  year         = {2015},
}

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