Control of networked traffic flow distribution - A stochastic distribution system perspective

Hong Wang; Stanley E. Young; H. M. Abdul Aziz; Sagar Patil

doi:10.1145/3109761.3158411

Control of networked traffic flow distribution - A stochastic distribution system perspective

Hong Wang, Stanley E. Young, H. M. Abdul Aziz, Sagar Patil

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic modelling and control for a one-way single-lane corridor traffic system based on theory of stochastic distribution control that shapes the PDF of the queue length.

Original language	English
Title of host publication	Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017
Editors	Hani Hamdan, Faouzi Hidoussi, Djallel Eddine Boubiche
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450352437
DOIs	https://doi.org/10.1145/3109761.3158411
State	Published - Oct 17 2017
Externally published	Yes
Event	1st International Conference on Internet of Things and Machine Learning, IML 2017 - Liverpool, United Kingdom Duration: Oct 17 2017 → Oct 18 2017

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	1st International Conference on Internet of Things and Machine Learning, IML 2017
Country/Territory	United Kingdom
City	Liverpool
Period	10/17/17 → 10/18/17

Funding

This work was supported by the EEMS program of the Vehicle Technologies Office of the Department of Energy, USA, this is gratefully acknowledged.

Keywords

Stochastic distribution control
Traffic queueing modelling

Access to Document

10.1145/3109761.3158411

Cite this

Wang, H., Young, S. E., Abdul Aziz, H. M., & Patil, S. (2017). Control of networked traffic flow distribution - A stochastic distribution system perspective. In H. Hamdan, F. Hidoussi, & D. E. Boubiche (Eds.), Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017 Article a37 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3109761.3158411

Wang, Hong ; Young, Stanley E. ; Abdul Aziz, H. M. et al. / Control of networked traffic flow distribution - A stochastic distribution system perspective. Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017. editor / Hani Hamdan ; Faouzi Hidoussi ; Djallel Eddine Boubiche. Association for Computing Machinery, 2017. (ACM International Conference Proceeding Series).

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title = "Control of networked traffic flow distribution - A stochastic distribution system perspective",

abstract = "At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic modelling and control for a one-way single-lane corridor traffic system based on theory of stochastic distribution control that shapes the PDF of the queue length.",

keywords = "Stochastic distribution control, Traffic queueing modelling",

author = "Hong Wang and Young, {Stanley E.} and {Abdul Aziz}, {H. M.} and Sagar Patil",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 1st International Conference on Internet of Things and Machine Learning, IML 2017 ; Conference date: 17-10-2017 Through 18-10-2017",

year = "2017",

month = oct,

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doi = "10.1145/3109761.3158411",

language = "English",

series = "ACM International Conference Proceeding Series",

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Wang, H, Young, SE, Abdul Aziz, HM & Patil, S 2017, Control of networked traffic flow distribution - A stochastic distribution system perspective. in H Hamdan, F Hidoussi & DE Boubiche (eds), Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017., a37, ACM International Conference Proceeding Series, Association for Computing Machinery, 1st International Conference on Internet of Things and Machine Learning, IML 2017, Liverpool, United Kingdom, 10/17/17. https://doi.org/10.1145/3109761.3158411

Control of networked traffic flow distribution - A stochastic distribution system perspective. / Wang, Hong; Young, Stanley E.; Abdul Aziz, H. M. et al.
Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017. ed. / Hani Hamdan; Faouzi Hidoussi; Djallel Eddine Boubiche. Association for Computing Machinery, 2017. a37 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic modelling and control for a one-way single-lane corridor traffic system based on theory of stochastic distribution control that shapes the PDF of the queue length.

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Wang H, Young SE, Abdul Aziz HM, Patil S. Control of networked traffic flow distribution - A stochastic distribution system perspective. In Hamdan H, Hidoussi F, Boubiche DE, editors, Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017. Association for Computing Machinery. 2017. a37. (ACM International Conference Proceeding Series). doi: 10.1145/3109761.3158411

Control of networked traffic flow distribution - A stochastic distribution system perspective

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