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Ballard Cordon Data Collection for Trucks and Cars (Task Order 8)

Start Date: January 2019
Funding: City of Seattle Department of Transportation (SDOT)
Project Budget: $50,000
Principal Investigator(s): Dr. Anne Goodchild

The Ballard Cordon Data Collection for Trucks and Cars is an analysis research project to be conducted by the Urban Freight Lab for the City of Seattle Department of Transportation (SDOT). Truck and car counts will be collected by reviewing video data for Major Truck Streets using the same Federal Highway Administration (FHWA) vehicle classification and additional large classifications as was developed and performed in the Greater Downtown Seattle Area Cordon Data Collection for Trucks and Cars project. This will enable SDOT to consider the impacts of various economic growth scenarios, advanced freight vehicle technologies, and other drivers (social, demographic, and policy changes) on truck routes.

Task 1 – Kickoff Meeting
SCTL will hold a kick-off meeting to:

  1. Identify count locations from which 48-hour and 72-hour data will be gathered and processed throughout the City.
  2. Identify prioritized count locations generally in the Ballard neighborhood and Ballard Interbay North Manufacturing and Industrial Center (BINMIC) for which a preliminary analysis will be provided.

Task 2 – Corridor Data Analysis
SCTL will review collected truck and car counts from video data recorded:

  1. SCTL will provide analysis regarding directionality, type, and any trends observed in the transcribed video based on developed typology of truck and van vehicle types for the video count data provided.
  2. The analysis will be divided into three categories:
    • A review of all cordon counts, including cordon counts around the downtown core
    • A review of Major Truck Street corridors on which counts were taken
    • A review of counts related to the BINMIC​

Task 3 – Reporting
The Urban Freight Lab will produce a written report documenting the methodology used and explaining the data collection, with simple descriptive statistics.