The Calculation Model of Freight Quantity Generation from a Survey of Beijing Restaurants

With the development of the urban catering industry, the proportion of restaurant freight quantity of a city’s total freight volume is rising rapidly. This paper aims to predict freight quantity by building a generation model based on multifactor analysis using data from a restaurant survey. The survey of 116 restaurants in different locations within the Fifth Ring Road of Beijing, was conducted from December 8 to 12, 2016. The model considers four factors: the number of employees, seat density, covered area, and operation times. Freight volume contains six kinds of ingredients: vegetables, meat, rice, flour, condiments, and drinks. The parameters of the freight generation model are calculated by regression analysis. The confidence coefficient of the model is measured through a T-test and thus the total freight quantity of Beijing restaurants can be predicted.

• Record URL:
  • https://doi.org/10.1061/9780784481523.050
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784481523
• Supplemental Notes:
  • © 2018 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhou, Yuyang
  • Xie, Ruxin
  • Tang, Songtao
  • Chen, Yanyan
• Conference:
  • 18th COTA International Conference of Transportation Professionals
  • Location: Beijing , China
  • Date: 2018-7-5 to 2018-7-8
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 499-507
• Monograph Title: CICTP 2018: Intelligence, Connectivity, and Mobility

Subject/Index Terms

• TRT Terms: Forecasting; Freight traffic; Restaurants; Traffic volume
• Geographic Terms: Beijing (China)
• Subject Areas: Freight Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01869384
• Record Type: Publication
• ISBN: 9780784481523
• Files: TRIS, ASCE
• Created Date: Dec 30 2022 4:59PM