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ANFIS based Short-term Prediction of Passenger Volume in Urban Rail Transit

byIISLab 2012.03.19 07:35
저자 Gyu-jin Jang, Jin-pyung Kim, Jee-Hyong Lee, Moon-hyun Kim 
학회명 International Symposium on Advanced Intelligent Systems 
학회명 (약자) ISIS 2011 
pp.  
학회시작일 2011-09-28 
학회종료일 2011-10-01 
비고  
Today, intelligent transportation system (ITS) has used public transport system for more efficient transportation. The prediction of passenger volume is the basis of intelligent transportation system. It needs to improve the quality of service in public transport system. The urban rail transit has become one of the most important parts of the public transit. Therefore, predicting passenger volume in urban rail transit is important to provide a service for public transport system and to make a transportation policy. In this paper, we proposed an ANFIS (Adaptive network-based Fuzzy Inference System) based prediction model to estimate passenger volume at subway station. The input data of proposed model consists of multiple temporal features with passenger quantity of each day in a year and rules generate 
by day of the week, season, day of the month and holiday. Experimental results show that accuracy of prediction is above 93% on two stations in Seoul Metropolitan Subway. 
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