Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data

Ryo Inoue; Akihisa Miyashita; Masatoshi Sugita

doi:10.1109/ITSC.2016.7795635

Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data

Ryo Inoue, Akihisa Miyashita, Masatoshi Sugita

INF - Human-Social Information Sciences

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

16 Citations (Scopus)

Abstract

Road traffic condition in cities are complicated by the daily, weekly, seasonally, and weather-induced traffic demand fluctuations and the effects caused by the control of traffic signals. Therefore, it is difficult to quantitatively analyze typical traffic congestion patterns that are represented by the time and place of occurrence, the process of propagation and diminution, duration time, and many others. This study proposed a method to enumerate traffic congestion patterns from traffic sensor data based on frequent pattern mining developed in information science to understand the present situations of traffic congestion in cities. The feasibility and effectiveness of the proposed method have been evaluated through the analysis of typical congestion patterns using the traffic sensor data in Okinawa, Japan.

Original language	English
Title of host publication	2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	731-736
Number of pages	6
ISBN (Electronic)	9781509018895
DOIs	https://doi.org/10.1109/ITSC.2016.7795635
Publication status	Published - 2016 Dec 22
Event	19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016 - Rio de Janeiro, Brazil Duration: 2016 Nov 1 → 2016 Nov 4

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Conference

Conference	19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
Country/Territory	Brazil
City	Rio de Janeiro
Period	16/11/1 → 16/11/4

UN SDGs

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

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10.1109/ITSC.2016.7795635

Cite this

Inoue, R., Miyashita, A., & Sugita, M. (2016). Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016 (pp. 731-736). Article 7795635 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2016.7795635

Inoue, Ryo ; Miyashita, Akihisa ; Sugita, Masatoshi. / Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data. 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 731-736 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

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title = "Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data",

abstract = "Road traffic condition in cities are complicated by the daily, weekly, seasonally, and weather-induced traffic demand fluctuations and the effects caused by the control of traffic signals. Therefore, it is difficult to quantitatively analyze typical traffic congestion patterns that are represented by the time and place of occurrence, the process of propagation and diminution, duration time, and many others. This study proposed a method to enumerate traffic congestion patterns from traffic sensor data based on frequent pattern mining developed in information science to understand the present situations of traffic congestion in cities. The feasibility and effectiveness of the proposed method have been evaluated through the analysis of typical congestion patterns using the traffic sensor data in Okinawa, Japan.",

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Inoue, R, Miyashita, A & Sugita, M 2016, Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data. in 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016., 7795635, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Institute of Electrical and Electronics Engineers Inc., pp. 731-736, 19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016, Rio de Janeiro, Brazil, 16/11/1. https://doi.org/10.1109/ITSC.2016.7795635

Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data. / Inoue, Ryo; Miyashita, Akihisa; Sugita, Masatoshi.
2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 731-736 7795635 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

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

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T1 - Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data

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AU - Sugita, Masatoshi

PY - 2016/12/22

Y1 - 2016/12/22

N2 - Road traffic condition in cities are complicated by the daily, weekly, seasonally, and weather-induced traffic demand fluctuations and the effects caused by the control of traffic signals. Therefore, it is difficult to quantitatively analyze typical traffic congestion patterns that are represented by the time and place of occurrence, the process of propagation and diminution, duration time, and many others. This study proposed a method to enumerate traffic congestion patterns from traffic sensor data based on frequent pattern mining developed in information science to understand the present situations of traffic congestion in cities. The feasibility and effectiveness of the proposed method have been evaluated through the analysis of typical congestion patterns using the traffic sensor data in Okinawa, Japan.

AB - Road traffic condition in cities are complicated by the daily, weekly, seasonally, and weather-induced traffic demand fluctuations and the effects caused by the control of traffic signals. Therefore, it is difficult to quantitatively analyze typical traffic congestion patterns that are represented by the time and place of occurrence, the process of propagation and diminution, duration time, and many others. This study proposed a method to enumerate traffic congestion patterns from traffic sensor data based on frequent pattern mining developed in information science to understand the present situations of traffic congestion in cities. The feasibility and effectiveness of the proposed method have been evaluated through the analysis of typical congestion patterns using the traffic sensor data in Okinawa, Japan.

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M3 - Conference contribution

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Inoue R, Miyashita A, Sugita M. Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 731-736. 7795635. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/ITSC.2016.7795635

Mining spatio-Temporal patterns of congested traffic in urban areas from traffic sensor data

Abstract

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