DocumentCode :
497428
Title :
Study on Bus Passenger Capacity Forecast Based on Regression Analysis including Time Series
Author :
Sun Wen-xia ; Song Ti ; Zhong Hai
Author_Institution :
Sch. of Manage., Tianjin Univ., Tianjin, China
Volume :
2
fYear :
2009
fDate :
11-12 April 2009
Firstpage :
381
Lastpage :
384
Abstract :
The passenger capacity in public transit is important to urban road and transportation planning. Passenger volume is influenced by multiple factors and unable to be described with accurate mathematical models. Regression analyses predict and time-series data analysis for forecasting are two important prediction models. These two models have characteristics of each. In this paper, time-series data was blend into regression analyses predict. Integrated the two forecasting methods, this paper combined the merits between them. Bus passenger capacity forecast model based on regression analysis including time series is put forward in this paper. This model better reflects the practical condition of bus passenger. The model passes through all tests. By analyzing the historic data of passenger volume of Xipsilaan, we study on bus passenger capacity forecast based on regression analysis including time series. The new model is fitting practical data more preferable.
Keywords :
forecasting theory; regression analysis; road vehicles; time series; transportation; bus passenger capacity forecasting; data analysis; mathematical model; prediction model; public transit; regression analysis; time series; transportation planning; urban road planning; Capacity planning; Data analysis; Predictive models; Regression analysis; Road transportation; Technology forecasting; Time measurement; Traffic control; Urban planning; Yttrium; least squares technique; prediction; regression analysis; time-series data; transit traffic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-0-7695-3583-8
Type :
conf
DOI :
10.1109/ICMTMA.2009.268
Filename :
5203452
Link To Document :
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