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Data Collection and Modeling of Restaurants’ Freight Trip Generation for Indian Cities

Data Collection and Modeling of Restaurants’ Freight Trip Generation for Indian Cities Freight transportation has received limited attention in the past compared to passenger transportation, especially in developing economies. However, the importance of freight transportation for the efficient functioning of any urban transportation system is gradually being realized. Estimating freight trips generated by the various manufacturing and service sectors in an urban setup is the primary step in freight transportation and management. For this purpose, segregating the various sectors and estimating freight trip generation is imperative. The primary aim of this study is to develop freight trip generation equations for one of the booming sectors in India, the restaurant service sector. The regions of Mumbai and Delhi-NCR are the focus of this study. About 150 restaurants (101 in Delhi-NCR, and 49 in Mumbai) were surveyed for this study. The face-to-face interview method at the establishments was adopted as the primary mode of data collection, primarily due to its high response rate. The daily average freight trips produced and attracted are observed to be approximately three vehicles and six vehicles, respectively. Separate models are estimated for freight trip attraction and production. It is observed that Poisson regression models for both attraction and production outperform the respective linear regression models. Poisson regression models are particularly useful when the dependent variable values are non-negative integers with sparse dispersion and a low mean. As far as the influencing variables are concerned, employment, vehicle ownership, and seating capacity are found to be significant for the freight trip models. The interaction variable formed by employment and vehicle ownership is used in the trip attraction model; similarly, a variable is created form the interaction of seating capacity and vehicle ownership in the trip production models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transportation in Developing Economies Springer Journals

Data Collection and Modeling of Restaurants’ Freight Trip Generation for Indian Cities

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References (34)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021
ISSN
2199-9287
eISSN
2199-9295
DOI
10.1007/s40890-021-00114-7
Publisher site
See Article on Publisher Site

Abstract

Freight transportation has received limited attention in the past compared to passenger transportation, especially in developing economies. However, the importance of freight transportation for the efficient functioning of any urban transportation system is gradually being realized. Estimating freight trips generated by the various manufacturing and service sectors in an urban setup is the primary step in freight transportation and management. For this purpose, segregating the various sectors and estimating freight trip generation is imperative. The primary aim of this study is to develop freight trip generation equations for one of the booming sectors in India, the restaurant service sector. The regions of Mumbai and Delhi-NCR are the focus of this study. About 150 restaurants (101 in Delhi-NCR, and 49 in Mumbai) were surveyed for this study. The face-to-face interview method at the establishments was adopted as the primary mode of data collection, primarily due to its high response rate. The daily average freight trips produced and attracted are observed to be approximately three vehicles and six vehicles, respectively. Separate models are estimated for freight trip attraction and production. It is observed that Poisson regression models for both attraction and production outperform the respective linear regression models. Poisson regression models are particularly useful when the dependent variable values are non-negative integers with sparse dispersion and a low mean. As far as the influencing variables are concerned, employment, vehicle ownership, and seating capacity are found to be significant for the freight trip models. The interaction variable formed by employment and vehicle ownership is used in the trip attraction model; similarly, a variable is created form the interaction of seating capacity and vehicle ownership in the trip production models.

Journal

Transportation in Developing EconomiesSpringer Journals

Published: Feb 14, 2021

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