Cloud-enabled vehicular congestion estimation: An ITS application

Milad Mahbadi; M. M.Manohara Pai; Sanoop Mallissery; Radhika M. Pai

doi:10.1109/CCECE.2016.7726829

Cloud-enabled vehicular congestion estimation: An ITS application

Milad Mahbadi, M. M.Manohara Pai, Sanoop Mallissery, Radhika M. Pai

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

3 Citations (Scopus)

Abstract

Increased traffic and hence congestion is a major problem in cities or urban areas. To mitigate this problem of congestion a real-time traffic density estimation model is essential. This research proposes one such model for estimating the traffic congestion level with the help of Vehicular Ad-hoc Network (VANET) and Cloud Computing. In this work, a novel architecture and algorithm has been proposed to estimate the density of vehicles on the road and the average speed. The fuzzy algorithm is then used to get the level of congestion. The algorithms are simulated using Network Simulator 3 (NS3) and Simulation of Urban Mobility (SUMO) to estimate the congestion level in an area of a city. The model proposed is deployed on Cloud and can be made available as Software as a Service (SaaS) in future.

Original language	English
Title of host publication	2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781467387217
DOIs	https://doi.org/10.1109/CCECE.2016.7726829
Publication status	Published - 3 Nov 2016
Externally published	Yes
Event	IEEE Canadian Conference on Electrical and Computer Engineering 2016 - Vancouver, Canada Duration: 15 May 2016 → 18 May 2016 Conference number: 29th https://ieeexplore.ieee.org/xpl/conhome/7589833/proceeding (Proceedings) http://ccece2016.ieee.ca (Website)

Publication series

Name	Canadian Conference on Electrical and Computer Engineering
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Volume	2016-October
ISSN (Print)	0840-7789

Conference

Conference	IEEE Canadian Conference on Electrical and Computer Engineering 2016
Abbreviated title	CCECE 2016
Country/Territory	Canada
City	Vancouver
Period	15/05/16 → 18/05/16
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7589833/proceeding (Proceedings) http://ccece2016.ieee.ca (Website)

Keywords

Density Estimation Model
Intelligent Transportation System
Traffic Congestion
VANET Cloud

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CCECE.2016.7726829

Cite this

Mahbadi, M., Pai, M. M. M., Mallissery, S., & Pai, R. M. (2016). Cloud-enabled vehicular congestion estimation: An ITS application. In 2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016 Article 7726829 (Canadian Conference on Electrical and Computer Engineering; Vol. 2016-October). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CCECE.2016.7726829

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title = "Cloud-enabled vehicular congestion estimation: An ITS application",

abstract = "Increased traffic and hence congestion is a major problem in cities or urban areas. To mitigate this problem of congestion a real-time traffic density estimation model is essential. This research proposes one such model for estimating the traffic congestion level with the help of Vehicular Ad-hoc Network (VANET) and Cloud Computing. In this work, a novel architecture and algorithm has been proposed to estimate the density of vehicles on the road and the average speed. The fuzzy algorithm is then used to get the level of congestion. The algorithms are simulated using Network Simulator 3 (NS3) and Simulation of Urban Mobility (SUMO) to estimate the congestion level in an area of a city. The model proposed is deployed on Cloud and can be made available as Software as a Service (SaaS) in future.",

keywords = "Density Estimation Model, Intelligent Transportation System, Traffic Congestion, VANET Cloud",

author = "Milad Mahbadi and Pai, \{M. M.Manohara\} and Sanoop Mallissery and Pai, \{Radhika M.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; IEEE Canadian Conference on Electrical and Computer Engineering 2016, CCECE 2016 ; Conference date: 15-05-2016 Through 18-05-2016",

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doi = "10.1109/CCECE.2016.7726829",

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series = "Canadian Conference on Electrical and Computer Engineering",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

booktitle = "2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016",

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Mahbadi, M, Pai, MMM, Mallissery, S & Pai, RM 2016, Cloud-enabled vehicular congestion estimation: An ITS application. in 2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016., 7726829, Canadian Conference on Electrical and Computer Engineering, vol. 2016-October, IEEE, Institute of Electrical and Electronics Engineers, IEEE Canadian Conference on Electrical and Computer Engineering 2016, Vancouver, British Columbia, Canada, 15/05/16. https://doi.org/10.1109/CCECE.2016.7726829

Cloud-enabled vehicular congestion estimation: An ITS application. / Mahbadi, Milad; Pai, M. M.Manohara; Mallissery, Sanoop et al.
2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016. IEEE, Institute of Electrical and Electronics Engineers, 2016. 7726829 (Canadian Conference on Electrical and Computer Engineering; Vol. 2016-October).

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

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T2 - IEEE Canadian Conference on Electrical and Computer Engineering 2016

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AU - Pai, Radhika M.

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N2 - Increased traffic and hence congestion is a major problem in cities or urban areas. To mitigate this problem of congestion a real-time traffic density estimation model is essential. This research proposes one such model for estimating the traffic congestion level with the help of Vehicular Ad-hoc Network (VANET) and Cloud Computing. In this work, a novel architecture and algorithm has been proposed to estimate the density of vehicles on the road and the average speed. The fuzzy algorithm is then used to get the level of congestion. The algorithms are simulated using Network Simulator 3 (NS3) and Simulation of Urban Mobility (SUMO) to estimate the congestion level in an area of a city. The model proposed is deployed on Cloud and can be made available as Software as a Service (SaaS) in future.

AB - Increased traffic and hence congestion is a major problem in cities or urban areas. To mitigate this problem of congestion a real-time traffic density estimation model is essential. This research proposes one such model for estimating the traffic congestion level with the help of Vehicular Ad-hoc Network (VANET) and Cloud Computing. In this work, a novel architecture and algorithm has been proposed to estimate the density of vehicles on the road and the average speed. The fuzzy algorithm is then used to get the level of congestion. The algorithms are simulated using Network Simulator 3 (NS3) and Simulation of Urban Mobility (SUMO) to estimate the congestion level in an area of a city. The model proposed is deployed on Cloud and can be made available as Software as a Service (SaaS) in future.

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KW - VANET Cloud

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Cloud-enabled vehicular congestion estimation: An ITS application

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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