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 contribution

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	Institute of Electrical and Electronics Engineers Inc.
Volume	2016-October
ISBN (Electronic)	9781467387217
DOIs	https://doi.org/10.1109/CCECE.2016.7726829
Publication status	Published - 31-10-2016
Event	2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016 - Vancouver, Canada Duration: 14-05-2016 → 18-05-2016

Conference

Conference	2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016
Country/Territory	Canada
City	Vancouver
Period	14-05-16 → 18-05-16

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Electrical and Electronic Engineering

UN SDGs

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

Access to Document

10.1109/CCECE.2016.7726829

Cite this

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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.",

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

year = "2016",

month = oct,

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doi = "10.1109/CCECE.2016.7726829",

language = "English",

volume = "2016-October",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016 ; Conference date: 14-05-2016 Through 18-05-2016",

}

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. vol. 2016-October, 7726829, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016, Vancouver, Canada, 14-05-16. 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. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. 7726829.

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

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T1 - Cloud-enabled vehicular congestion estimation

T2 - 2016 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2016

AU - Mahbadi, Milad

AU - Pai, M. M.Manohara

AU - Mallissery, Sanoop

AU - Pai, Radhika M.

PY - 2016/10/31

Y1 - 2016/10/31

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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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Cloud-enabled vehicular congestion estimation: An ITS application

Abstract

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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