{"title":"Daily visitor volume forecasts for Expo 2010 Shanghai China","authors":"Yang Zhang","doi":"10.1109/ITSC.2011.6082994","DOIUrl":null,"url":null,"abstract":"The long term and great number of visitors to World Exposition 2010 Shanghai China (World Expo 2010) brought additional pressure to the regular urban traffic. This study provided daily visitor volume forecasts before the Expo Site opened each day. Related government departments benefited from the prediction in the management of Expo park service system and transportation scheduling. According to the natural classification of expo visitors into individuals and groups, the letter applied a hybrid methodology of fuzzy Takagi-Sugeno (T-S) models and linear least squares regression (LLSR) model to obtain the forecasts. The proposed approach showed the capacity of highly accurate prediction and remarkable robustness. And the results were timely issued through the Comprehensive Transportation Information Platform (CTIP) to the Shanghai government and Bureau of Shanghai World Expo Coordination for reference.","PeriodicalId":186596,"journal":{"name":"2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)","volume":"432 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2011.6082994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The long term and great number of visitors to World Exposition 2010 Shanghai China (World Expo 2010) brought additional pressure to the regular urban traffic. This study provided daily visitor volume forecasts before the Expo Site opened each day. Related government departments benefited from the prediction in the management of Expo park service system and transportation scheduling. According to the natural classification of expo visitors into individuals and groups, the letter applied a hybrid methodology of fuzzy Takagi-Sugeno (T-S) models and linear least squares regression (LLSR) model to obtain the forecasts. The proposed approach showed the capacity of highly accurate prediction and remarkable robustness. And the results were timely issued through the Comprehensive Transportation Information Platform (CTIP) to the Shanghai government and Bureau of Shanghai World Expo Coordination for reference.