Multiple Linear Regression Analysis of Canada’s Freight Transportation Framework

Jamileh Yousefi (), Sahand Ashtab (), Amirali Yasaei, Allu George, Ali Mukarram and Satinderpal Singh Sandhu
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Jamileh Yousefi: Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada
Sahand Ashtab: Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada
Amirali Yasaei: Faculty of Engineering, Waterloo University, Waterloo, ON N2L 3G1, Canada
Allu George: Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada
Ali Mukarram: Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada
Satinderpal Singh Sandhu: Shannon School of Business, Cape Breton University, Sydney, NS B1P 6L2, Canada

Logistics, 2023, vol. 7, issue 2, 1-17

Abstract: 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.

Keywords: transportation; Canada; truck; multiple regression; analytics; linear regression (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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