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

Content Provider	IET Digital Library
Author	Sutarto, Herman Yoseph Boel, René K. Joelianto, Endra
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.
Starting Page	1683
Ending Page	1691
Page Count	9
ISSN	17518644
Volume Number	9
e-ISSN	17518652
Issue Number	Issue 11, Jul (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/9/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2014.0909
Journal	IET Control Theory & Applications
Publisher Date	2015-05-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Model-based Filter Adaptive Traffic Flow State Estimator Algebra AR Stochastic Process Autoregressive Processes Data-based Approach EM Parameter Estimation EM Technique Expectation-maximisation Algorithm Expectation-maximisation Technique Feedback Control First-order Markov Chain Jump Markov Process Markov Processes Matrix Algebra Mode Switching Mode-dependent First-order Autoregressive Stochastic Process Modelling And Identification Online Particle Filter Parameter Estimation Particle Filter Road Traffic Signal Processing Theory Signalised Intersections Simulation Smoothed Inference Algorithm Stochastic Hybrid Model System Theory Application in Transportation Time-window Shift Technique Traffic Flow Rate Traffic Lights Transition Matrix Estimation Urban Traffic Flow Estimation Urban Traffic Network
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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