Using Big Data to Develop Comparative Commercial Vehicle Metrics for Port Traffic at Major Ports in the U.S.

This paper utilizes archival, anonymous, Big Data derived from GPS management systems in commercial vehicles to analyze key trends and indicators for six major ports in the US. The paper first defines key metrics of interest to stakeholders in port communities – including “trade area” for trucks affiliated with each port and understanding the share of port-affiliated vehicles miles travelled and trips within various ranges of the port. Next, the paper creates these metrics for each of the six ports using GPS data from fleet management tools. Finally, the paper compares trends found across different ports, port types, and vehicle types and the implications for port communities. The authors find that developing such metrics is straightforward, expandable to other ports in the US, and that comparisons and benchmarking between ports yields useful insights both to individual port communities and to broader national audiences. Furthermore, while ports were chosen because they offer centralized opportunities to understand, intervene, and study the impact of vehicles, the approaches described can be extended to other types of places.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ90 Standing Committee on Freight Transportation Data.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Komanduri, Anurag
  • Schewel, Laura
  • Beagan, Dan
  • Wong, Dale
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Benchmarks; Global Positioning System; Metrics (Quantitative assessment); Ports; Traffic data; Truck traffic; Trucking
• Candidate Terms: Geospatial analysis
• Identifier Terms: Port of Long Beach; Port of Los Angeles
• Subject Areas: Data and Information Technology; Freight Transportation;

Filing Info

• Accession Number: 01627589
• Record Type: Publication
• Report/Paper Numbers: 17-04629
• Files: TRIS, TRB, ATRI
• Created Date: Feb 27 2017 5:12PM