NDLI: Linear regression prediction model of prefecture level highway passenger transport volume

Content Provider	IEEE Xplore Digital Library
Author	Shuangying Xu Jian Ma Lei Zhang Xiaolin Gao
Copyright Year	2011
Description	Author affiliation: School of automobile, Chang'an University, Xi'an Shaanxi, China (Shuangying Xu; Jian Ma; Lei Zhang; Xiaolin Gao)
Abstract	Prefecture level highway passenger transport occupied an important position in China highway transportation system. Correct forecast growth of prefecture highway passenger transport volume was the key for prefecture passenger planning and optimizing transport resource allocation. This paper analyzed the influence factors of prefecture level highway passenger transport volume and constructed linear regression prediction model of passenger transport volume. The model was calibrated by using specific prefecture highway passenger transport volume and socioeconomic data for 11 consecutive years by EXCEL. The highway passenger transport volumes of prospective years were forecasted. Example application show that explanatory variables are different because of different industy structure and economic activity economic levels. The calibrated regression model accurately reflects the economic and social reasons of the highway passenger transport volumes growth and the highway passenger transport volumes growth forecast has good explanatory.
Starting Page	3250
Ending Page	3253
File Size	736145
Page Count	4
File Format	PDF
ISBN	9781612840871
e-ISBN	9781612840888
DOI	10.1109/EMEIT.2011.6023040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Road transportation Economics Solid modeling Linear regression prediction model Demand forecast Predictive models Prefecture level highway passenger transport volume Data models Highway transport Mathematical model Equations
Content Type	Text
Resource Type	Article

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