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

Ryo Inoue, Akihisa Miyashita, Masatoshi Sugita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 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 languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages731-736
Number of pages6
ISBN (Electronic)9781509018895
DOIs
Publication statusPublished - 2016 Dec 22
Event19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016 - Rio de Janeiro, Brazil
Duration: 2016 Nov 12016 Nov 4

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Other

Other19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
CountryBrazil
CityRio de Janeiro
Period16/11/116/11/4

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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