Using Big Data to Develop Commercial Vehicle Metrics at Ports in the US
Corresponding Author: Anurag Komanduri, Cambridge Systematics, Inc.
Presented By: Anurag Komanduri, Cambridge Systematics
Abstract
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 traveled 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 10 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 areas, including communities, corridors, and activity centers.