An Automatic Pattern Recognition Algorithm for Identifying the Spatio-temporal Congestion Evolution Patterns in Freeway Historic Data

고속도로 이력데이터에 포함된 정체 시공간 전개 패턴 자동인식 알고리즘 개발

  • Park, Eun Mi (Department of Urban Engineering, Mokwon University) ;
  • Oh, Hyun Sun (Department of Urban Engineering, Mokwon University)
  • 박은미 (목원대학교 도시공학과) ;
  • 오현선 (목원대학교 도시공학과)
  • Received : 2014.07.23
  • Accepted : 2014.10.08
  • Published : 2014.10.31


Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.


Supported by : Ministry of Land, Infrastructure and Transport


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