A Comprehensive Business Location Choice Model Leveraging Machine Learning in Systematic Choice Set

This study develops a comprehensive two-stage location choice framework for business establishments as part of a goods movements modeling. This study aims to formulate a systematic methodology for investigating the location choice of business establishments within Halifax Regional Municipality. This study presents a novel approach that leverages machine learning techniques to generate a systematic choice set, thereby improving the representation of realistic and reasonable location alternatives. Info Canada Business Establishments dataset 2022 is employed to achieve the aim of this study. Combining an unsupervised machine learning technique with the mixed multinomial logit model facilitates a data-driven approach to enhance the precision and robustness of business establishment location choice models. This approach possesses the potential to unveil latent patterns and heterogeneity among potential choice alternatives that may remain obscured when utilizing a conventional multinomial logit model. This thorough analysis offers robust insights into the factors influencing the location choice of business establishments. The findings obtained from this comprehensive study suggest that wholesalers prioritize proximity to highways and positions within business parks for their operations while avoiding higher population density and central business district proximity. Transportation businesses seek larger sites and locations near highways, favoring clustering with related transport companies and valuing accessibility and cost-effectiveness over proximity to business parks or rural settings. The findings of this study could provide valuable insights for commercial vehicle and goods movement modeling, business location strategies, and policymaking concerning sustainable urban development.

• Record URL:
  • https://doi.org/10.1177/03611981241253609
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Niaz Mahmud https://orcid.org/0000-0002-2538-5045© The Author(s) 2024.
• Authors:
  • Mahmud, Niaz
  • 0000-0002-2538-5045
  • Habib, Muhammad Ahsanul
  • 0000-0003-1461-9552
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: pp 1856-1871
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2678
  • Issue Number: 12
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Businesses; Choice models; Freight traffic; Location; Machine learning
• Geographic Terms: Halifax (Canada)
• Subject Areas: Freight Transportation; Planning and Forecasting; Terminals and Facilities;

Filing Info

• Accession Number: 01925975
• Record Type: Publication
• Files: TRIS, TRB
• Created Date: Jul 30 2024 9:53AM