Multiple Linear Regression Analysis of Canada’s Freight Transportation Framework

Logistics

article

Jamileh Yousefi¹  Sahand Ashtab¹  Amirali Yasaei²  Allu George¹  Ali Mukarram¹  Satinderpal Singh Sandhu¹ 

[1] Shannon School of Business, Cape Breton University;Faculty of Engineering, Waterloo University

关键词: transportation;    Canada;    truck;    multiple regression;    analytics;    linear regression;   

DOI  :  10.3390/logistics7020029

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Background: Finding trends in freight transportation activities enables businesses and policy makers to build an understanding of freight transportation patterns and their impact on logistics planning when making investments in a region’s transportation infrastructure and intermodal freight transport system. To the best of our knowledge, there is limited literature and data-driven analysis about trends in transportation mode choices and the influencing factors in Atlantic Canada. Methods: In this study, a data-driven method has been used to analyze the Canadian Freight dataset to identify trends in transportation activities within Maritime, Canada. Freight transportation mode, product categories, distance, number/weight of shipments, and revenue were examined. Results: The results revealed that the top five product categories exported from Atlantic provinces to the rest of Canada, the US, and Mexico are miscellaneous items, food products, forest products, minerals, and other manufactured goods, where Truck for Hire is the most deployed mode of transportation. A multiple linear regression analysis indicated that the weight, distance, and number of shipments are positively and rather strongly correlated with revenue generation. Conclusions: This study provides a unique overview of Canadian Freight Analysis Framework (CFAF) data with a focus on maritime activities.

【 授权许可】

CC BY