Using Crowd Sensed Data as Input to Congestion Model

Anders Lehmann; Allan Gross

doi:10.1109/PERCOMW.2016.7457105

Using Crowd Sensed Data as Input to Congestion Model

Anders Lehmann, Allan Gross

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

3 Citations (Scopus)

440 Downloads (Pure)

Abstract

Emission of airborne pollutants and climate gasses from the transport sector is a growing problem, both in indus- trialised and developing countries. Planning of urban transport system is essential to minimise the environmental, health and economic impact of congestion in the transport system. To get accurate and timely information on traffic congestion, and by extension information on air pollution, near real time traffic models are needed. We present in this paper an implementation of the Restricted Stochastic User equilibrium model, that is capable to model congestions for very large Urban traffic systems, in less than an hour. The model is implemented in an open source database system, for easy interface with GIS resources and crowd sensed transportation data.

Original language	English
Title of host publication	2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 : The Third International Workshop on Crowd Assisted Sensing Pervasive Systems and Communications
Number of pages	6
Publisher	IEEE Press
Publication date	10 Apr 2016
Article number	7457105
ISBN (Electronic)	978-1-5090-1941-0
DOIs	https://doi.org/10.1109/PERCOMW.2016.7457105
Publication status	Published - 10 Apr 2016
Event	2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) - Sydney, Australia Duration: 14 Mar 2016 → 18 Mar 2016 http://www.percom.org/Previous/ST2016/

Workshop

Workshop	2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)
Country/Territory	Australia
City	Sydney
Period	14/03/2016 → 18/03/2016
Internet address	http://www.percom.org/Previous/ST2016/

Access to Document

10.1109/PERCOMW.2016.7457105

CASPER16

Cite this

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title = "Using Crowd Sensed Data as Input to Congestion Model",

abstract = "Emission of airborne pollutants and climate gasses from the transport sector is a growing problem, both in indus- trialised and developing countries. Planning of urban transport system is essential to minimise the environmental, health and economic impact of congestion in the transport system. To get accurate and timely information on traffic congestion, and by extension information on air pollution, near real time traffic models are needed. We present in this paper an implementation of the Restricted Stochastic User equilibrium model, that is capable to model congestions for very large Urban traffic systems, in less than an hour. The model is implemented in an open source database system, for easy interface with GIS resources and crowd sensed transportation data.",

author = "Anders Lehmann and Allan Gross",

year = "2016",

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day = "10",

doi = "10.1109/PERCOMW.2016.7457105",

language = "English",

booktitle = "2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016",

publisher = "IEEE Press",

note = "2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) ; Conference date: 14-03-2016 Through 18-03-2016",

url = "http://www.percom.org/Previous/ST2016/",

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Lehmann, A & Gross, A 2016, Using Crowd Sensed Data as Input to Congestion Model. in 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016: The Third International Workshop on Crowd Assisted Sensing Pervasive Systems and Communications., 7457105, IEEE Press, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia, 14/03/2016. https://doi.org/10.1109/PERCOMW.2016.7457105

Using Crowd Sensed Data as Input to Congestion Model. / Lehmann, Anders; Gross, Allan.
2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016: The Third International Workshop on Crowd Assisted Sensing Pervasive Systems and Communications. IEEE Press, 2016. 7457105.

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

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N2 - Emission of airborne pollutants and climate gasses from the transport sector is a growing problem, both in indus- trialised and developing countries. Planning of urban transport system is essential to minimise the environmental, health and economic impact of congestion in the transport system. To get accurate and timely information on traffic congestion, and by extension information on air pollution, near real time traffic models are needed. We present in this paper an implementation of the Restricted Stochastic User equilibrium model, that is capable to model congestions for very large Urban traffic systems, in less than an hour. The model is implemented in an open source database system, for easy interface with GIS resources and crowd sensed transportation data.

AB - Emission of airborne pollutants and climate gasses from the transport sector is a growing problem, both in indus- trialised and developing countries. Planning of urban transport system is essential to minimise the environmental, health and economic impact of congestion in the transport system. To get accurate and timely information on traffic congestion, and by extension information on air pollution, near real time traffic models are needed. We present in this paper an implementation of the Restricted Stochastic User equilibrium model, that is capable to model congestions for very large Urban traffic systems, in less than an hour. The model is implemented in an open source database system, for easy interface with GIS resources and crowd sensed transportation data.

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Using Crowd Sensed Data as Input to Congestion Model

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EcoSense: EcoSense

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Using Crowd Sensed Data as Input to Congestion Model

Abstract

Workshop

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EcoSense: EcoSense

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