Partitioning a road network for distributed processing for large-scale traffic flow prediction

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  • ISHIGURO Futoshi
    Graduate School of Informatics, Nagoya University
  • WATANABE Yousuke
    Institutes of Innovation for Future Society, Nagoya University
  • TAKADA Hiroaki
    Graduate School of Informatics, Nagoya University,Institutes of Innovation for Future Society, Nagoya University

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Other Title
  • 大規模交通流予測の分散処理のための道路ネットワーク分割手法
  • ダイキボ コウツウリュウ ヨソク ノ ブンサン ショリ ノ タメ ノ ドウロ ネットワーク ブンカツ シュホウ

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Abstract

<p>Recently, many cars and road infrastructures have collected traffic data. Furthermore, traffic flow prediction using these data has been the focus of many studies. Traffic flow prediction is useful in avoiding traffic jams and suggesting an efficient route. However, large-scale traffic flow prediction takes much execution time. This paper proposes a method of partitioning a road network for distributed processing for large-scale traffic flow prediction. Our method consists two steps : (1) Partitioning the process of training models; (2) Selecting input data for each model. Our experimental evaluation shows that the method successfully reduces execution time. Too much input data does not improve prediction accuracy. Moreover, some input data is unrelated to distance between roads. </p>

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