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Subway Congestion Prediction and Recommendation System using Big Data Analysis

빅데이터 분석을 이용한 지하철 혼잡도 예측 및 추천시스템

  • Kim, Jin-su (College of Liberal Arts, Anyang University)
  • Received : 2016.10.02
  • Accepted : 2016.11.20
  • Published : 2016.11.28

Abstract

Subway is a future-oriented means of transportation that can be safely and quickly mass transport many passengers than buses and taxis. Congestion growth due to the increase of the metro users is one of the factors that hinder citizens' rights to comfortably use the subway. Accordingly, congestion prediction in the subway is one of the ways to maximize the use of passenger convenience and comfort. In this paper, we monitor the level of congestion in real time via the existing congestion on the metro using multiple regression analysis and big data processing, as well as their departure station and arrival station information More information about the transfer stations offer a personalized congestion prediction system. The accuracy of the predicted congestion shows about 81% accuracy, which is compared to the real congestion. In this paper, the proposed prediction and recommendation application will be a help to prediction of subway congestion and user convenience.

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