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Particle filter state estimator for large urban networks

Nicolae-Emanuel Marinica (UGent) and René Boel (UGent)
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Abstract
This paper applies a particle filter (PF) state estimator to urban traffic networks. The traffic network consists of signalized intersections, the roads that link these intersections, and sensors that detect the passage time of vehicles. The traffic state X(t) specifies at each time time t the state of the traffic lights, the queue sizes at the intersections, and the location and size of all the platoons of vehicles inside the system. The basic entity of our model is a platoon of vehicles that travel close together at approximately the same speed. This leads to a discrete event simulation model that is much faster than microscopic models representing individual vehicles. Hence it is possible to execute many random simulation runs in parallel. A particle filter (PF) assigns weights to each of these simulation runs, according to how well they explain the observed sensor signals. The PF thus generates estimates at each time t of the location of the platoons, and more importantly the queue size at each intersection. These estimates can be used for controlling the optimal switching times of the traffic light.
Keywords
stochastic systems, platoon based model, Bayesian estimation, particle filtering, urban traffic

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

MLA
Marinica, Nicolae-Emanuel, and René Boel. “Particle Filter State Estimator for Large Urban Networks.” Proceedings of the 2011 Australian Control Conference (AUCC 2011). Piscataway, NJ, USA: IEEE, 2011. 374–380. Print.
APA
Marinica, N.-E., & Boel, R. (2011). Particle filter state estimator for large urban networks. Proceedings of the 2011 Australian Control Conference (AUCC 2011) (pp. 374–380). Presented at the 2011 Australian Control Conference (AUCC 2011), Piscataway, NJ, USA: IEEE.
Chicago author-date
Marinica, Nicolae-Emanuel, and René Boel. 2011. “Particle Filter State Estimator for Large Urban Networks.” In Proceedings of the 2011 Australian Control Conference (AUCC 2011), 374–380. Piscataway, NJ, USA: IEEE.
Chicago author-date (all authors)
Marinica, Nicolae-Emanuel, and René Boel. 2011. “Particle Filter State Estimator for Large Urban Networks.” In Proceedings of the 2011 Australian Control Conference (AUCC 2011), 374–380. Piscataway, NJ, USA: IEEE.
Vancouver
1.
Marinica N-E, Boel R. Particle filter state estimator for large urban networks. Proceedings of the 2011 Australian Control Conference (AUCC 2011). Piscataway, NJ, USA: IEEE; 2011. p. 374–80.
IEEE
[1]
N.-E. Marinica and R. Boel, “Particle filter state estimator for large urban networks,” in Proceedings of the 2011 Australian Control Conference (AUCC 2011), Melbourne, VIC, Australia, 2011, pp. 374–380.
@inproceedings{1993807,
  abstract     = {This paper applies a particle filter (PF) state estimator to urban traffic networks. The traffic network consists of signalized intersections, the roads that link these intersections, and sensors that detect the passage time of vehicles. The traffic state X(t) specifies at each time time t the state of the traffic lights, the queue sizes at the intersections, and the location and size of all the platoons of vehicles inside the system. The basic entity of our model is a platoon of vehicles that travel close together at approximately the same speed. This leads to a discrete event simulation model that is much faster than microscopic models representing individual vehicles. Hence it is possible to execute many random simulation runs in parallel. A particle filter (PF) assigns weights to each of these simulation runs, according to how well they explain the observed sensor signals. The PF thus generates estimates at each time t of the location of the platoons, and more importantly the queue size at each intersection. These estimates can be used for controlling the optimal switching times of the traffic light.},
  author       = {Marinica, Nicolae-Emanuel and Boel, René},
  booktitle    = {Proceedings of the 2011 Australian Control Conference (AUCC 2011)},
  isbn         = {9781424492459},
  keywords     = {stochastic systems,platoon based model,Bayesian estimation,particle filtering,urban traffic},
  language     = {eng},
  location     = {Melbourne, VIC, Australia},
  pages        = {374--380},
  publisher    = {IEEE},
  title        = {Particle filter state estimator for large urban networks},
  year         = {2011},
}