Understanding daily mobility patterns in urban road networks using traffic flow analytics

Ibai Laña; Javier Del Ser; Ignacio Iñaki Olabarrieta

doi:10.1109/NOMS.2016.7502980

Understanding daily mobility patterns in urban road networks using traffic flow analytics

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

34 Citations (Scopus)

Abstract

The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.

Original language	English
Title of host publication	Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
Editors	Sema Oktug Badonnel, Mehmet Ulema, Cicek Cavdar, Lisandro Zambenedetti Granville, Carlos Raniery P. dos Santos
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1157-1162
Number of pages	6
ISBN (Electronic)	9781509002238
DOIs	https://doi.org/10.1109/NOMS.2016.7502980
Publication status	Published - 30 Jun 2016
Event	2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016 - Istanbul, Turkey Duration: 25 Apr 2016 → 29 Apr 2016

Publication series

Name	Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Conference

Conference	2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016
Country/Territory	Turkey
City	Istanbul
Period	25/04/16 → 29/04/16

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

Long-term traffic flow prediction
machine learning
mobility patterns

Access to Document

10.1109/NOMS.2016.7502980

Cite this

Laña, I., Del Ser, J., & Olabarrieta, I. I. (2016). Understanding daily mobility patterns in urban road networks using traffic flow analytics. In S. O. Badonnel, M. Ulema, C. Cavdar, L. Z. Granville, & C. R. P. dos Santos (Eds.), Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium (pp. 1157-1162). Article 7502980 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NOMS.2016.7502980

Laña, Ibai ; Del Ser, Javier ; Olabarrieta, Ignacio Iñaki. / Understanding daily mobility patterns in urban road networks using traffic flow analytics. Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. editor / Sema Oktug Badonnel ; Mehmet Ulema ; Cicek Cavdar ; Lisandro Zambenedetti Granville ; Carlos Raniery P. dos Santos. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1157-1162 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium).

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title = "Understanding daily mobility patterns in urban road networks using traffic flow analytics",

abstract = "The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.",

keywords = "Long-term traffic flow prediction, machine learning, mobility patterns",

author = "Ibai La{\~n}a and \{Del Ser\}, Javier and Olabarrieta, \{Ignacio I{\~n}aki\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016 ; Conference date: 25-04-2016 Through 29-04-2016",

year = "2016",

month = jun,

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doi = "10.1109/NOMS.2016.7502980",

language = "English",

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Laña, I, Del Ser, J & Olabarrieta, II 2016, Understanding daily mobility patterns in urban road networks using traffic flow analytics. in SO Badonnel, M Ulema, C Cavdar, LZ Granville & CRP dos Santos (eds), Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium., 7502980, Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, Institute of Electrical and Electronics Engineers Inc., pp. 1157-1162, 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016, Istanbul, Turkey, 25/04/16. https://doi.org/10.1109/NOMS.2016.7502980

Understanding daily mobility patterns in urban road networks using traffic flow analytics. / Laña, Ibai; Del Ser, Javier ; Olabarrieta, Ignacio Iñaki.
Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. ed. / Sema Oktug Badonnel; Mehmet Ulema; Cicek Cavdar; Lisandro Zambenedetti Granville; Carlos Raniery P. dos Santos. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1157-1162 7502980 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium).

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

TY - GEN

T1 - Understanding daily mobility patterns in urban road networks using traffic flow analytics

AU - Laña, Ibai

AU - Del Ser, Javier

AU - Olabarrieta, Ignacio Iñaki

PY - 2016/6/30

Y1 - 2016/6/30

N2 - The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.

AB - The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.

KW - Long-term traffic flow prediction

KW - machine learning

KW - mobility patterns

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DO - 10.1109/NOMS.2016.7502980

M3 - Conference contribution

AN - SCOPUS:84979742597

T3 - Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

SP - 1157

EP - 1162

BT - Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

A2 - Badonnel, Sema Oktug

A2 - Ulema, Mehmet

A2 - Cavdar, Cicek

A2 - Granville, Lisandro Zambenedetti

A2 - dos Santos, Carlos Raniery P.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016

Y2 - 25 April 2016 through 29 April 2016

ER -

Laña I, Del Ser J , Olabarrieta II. Understanding daily mobility patterns in urban road networks using traffic flow analytics. In Badonnel SO, Ulema M, Cavdar C, Granville LZ, dos Santos CRP, editors, Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1157-1162. 7502980. (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium). doi: 10.1109/NOMS.2016.7502980

Understanding daily mobility patterns in urban road networks using traffic flow analytics

Abstract

Publication series

Conference

UN SDGs

Keywords

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