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Developing a Stochastic Model of Queue Length at a Signalized Intersection

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@article{IJASEIT2820,
   author = {Herman Y Sutarto and Endra Joelianto and Tunggul Arief Nugroho},
   title = {Developing a Stochastic Model of Queue Length at a Signalized Intersection},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {6},
   year = {2017},
   pages = {2183--2188},
   keywords = {queue length model; stochastic hybrid; particle filter.},
   abstract = {This paper proposes a stochastic hybrid dynamic model of the queue-length at a signalized intersection. The flow rate along with traffic light variables are used to define the evolution of the queue-lengths and it evolves as a piecewise linear function, being the integral of the difference between arrival and departure rate; these arrival and departure rates are described by stochastic AR model with mode-dependent parameters. The mode changes are modeled by a first order 2 or 3-state Markov process. The traffic flow rate is described using a mode-dependent first autoregressive (AR) stochastic process. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia and synthetic data from VISSIM traffic simulator. The model thus obtained via EM parameter estimation is validated by using the online particle filter. This technique can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator as crucial part for the synthesis of traffic light control.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2820},
   doi = {10.18517/ijaseit.7.6.2820}
}

EndNote

%A Sutarto, Herman Y
%A Joelianto, Endra
%A Nugroho, Tunggul Arief
%D 2017
%T Developing a Stochastic Model of Queue Length at a Signalized Intersection
%B 2017
%9 queue length model; stochastic hybrid; particle filter.
%! Developing a Stochastic Model of Queue Length at a Signalized Intersection
%K queue length model; stochastic hybrid; particle filter.
%X This paper proposes a stochastic hybrid dynamic model of the queue-length at a signalized intersection. The flow rate along with traffic light variables are used to define the evolution of the queue-lengths and it evolves as a piecewise linear function, being the integral of the difference between arrival and departure rate; these arrival and departure rates are described by stochastic AR model with mode-dependent parameters. The mode changes are modeled by a first order 2 or 3-state Markov process. The traffic flow rate is described using a mode-dependent first autoregressive (AR) stochastic process. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia and synthetic data from VISSIM traffic simulator. The model thus obtained via EM parameter estimation is validated by using the online particle filter. This technique can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator as crucial part for the synthesis of traffic light control.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2820
%R doi:10.18517/ijaseit.7.6.2820
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 6
%@ 2088-5334

IEEE

Herman Y Sutarto,Endra Joelianto and Tunggul Arief Nugroho,"Developing a Stochastic Model of Queue Length at a Signalized Intersection," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, pp. 2183-2188, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.6.2820.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sutarto, Herman Y
AU  - Joelianto, Endra
AU  - Nugroho, Tunggul Arief
PY  - 2017
TI  - Developing a Stochastic Model of Queue Length at a Signalized Intersection
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 6
Y2  - 2017
SP  - 2183
EP  - 2188
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - queue length model; stochastic hybrid; particle filter.
N2  - This paper proposes a stochastic hybrid dynamic model of the queue-length at a signalized intersection. The flow rate along with traffic light variables are used to define the evolution of the queue-lengths and it evolves as a piecewise linear function, being the integral of the difference between arrival and departure rate; these arrival and departure rates are described by stochastic AR model with mode-dependent parameters. The mode changes are modeled by a first order 2 or 3-state Markov process. The traffic flow rate is described using a mode-dependent first autoregressive (AR) stochastic process. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia and synthetic data from VISSIM traffic simulator. The model thus obtained via EM parameter estimation is validated by using the online particle filter. This technique can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator as crucial part for the synthesis of traffic light control.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2820
DO  - 10.18517/ijaseit.7.6.2820

RefWorks

RT Journal Article
ID 2820
A1 Sutarto, Herman Y
A1 Joelianto, Endra
A1 Nugroho, Tunggul Arief
T1 Developing a Stochastic Model of Queue Length at a Signalized Intersection
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 6
YR 2017
SP 2183
OP 2188
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 queue length model; stochastic hybrid; particle filter.
AB This paper proposes a stochastic hybrid dynamic model of the queue-length at a signalized intersection. The flow rate along with traffic light variables are used to define the evolution of the queue-lengths and it evolves as a piecewise linear function, being the integral of the difference between arrival and departure rate; these arrival and departure rates are described by stochastic AR model with mode-dependent parameters. The mode changes are modeled by a first order 2 or 3-state Markov process. The traffic flow rate is described using a mode-dependent first autoregressive (AR) stochastic process. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia and synthetic data from VISSIM traffic simulator. The model thus obtained via EM parameter estimation is validated by using the online particle filter. This technique can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator as crucial part for the synthesis of traffic light control.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2820
DO  - 10.18517/ijaseit.7.6.2820