Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment

As e-commerce and urban deliveries spike, there is an increasing demand for curbside loading/unloading space. At the same time, cities grapple with managing urban freight more actively, and need to better understand commercial vehicle (CV) driver behaviors and factors affecting their parking decisions. CV drivers face numerous challenges and have to adopt different travel and parking behaviors to perform deliveries and pick-ups efficiently. A recent study by the Urban Freight Lab (UFL) identified three main criteria CV drivers use when choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other road users, and competition with other CV drivers. UFL researchers also found that in response to the lack of available parking, drivers take one of the following actions: unauthorized parking, cruising for parking, queueing, and re-routing. The literature on decision-making process and parking behavior of CV drivers is scarce, and the data for such studies usually come from empirical field studies, while there are only limited situations that can be observed in existing situations, and even with those, driver characteristics remain mostly unknown. The proposed study will simulate several parking situations for CV drivers and analyze their reactions. The simulation will be designed in a quarter-cab truck simulator at Oregon State University. Various simulation environments will be defined by changing road characteristics (e.g. land use, number of travel lanes, nearby signals), curb allocations and their availability (e.g. paid parking, passenger and commercial load zones), and other road users. Drivers from various categories of age, gender, experience level (less experiences vs. seasoned) and goods type (documents, packages, or heavy goods) will be invited to operate the simulator and make a parking decision in a few simulated environments. The simulator can also monitor distraction (through eye tracking) and the stress level of drivers (through galvanic skin response) when making these decisions and interacting with other road users. Analyzing parking decisions and driver stress levels based on roadway and driver characteristics will provide insights on travel behaviors and the parking decision-making process of CV drivers, and will help city planners develop better curb management policies to accommodate safe and efficient operations in a shared urban roadway environment.

Language

  • English

Project

  • Status: Active
  • Funding: $360000
  • Contract Numbers:

    69A3551747110

  • Sponsor Organizations:

    Pacific Northwest Transportation Consortium

    University of Washington
    More Hall Room 112
    Seattle, WA  United States  98195-2700

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    University of Washington, Seattle

    Civil and Environmental Engineering Department
    201 More Hall, Box 352700
    Seattle, WA  United States  98195-2700
  • Project Managers:

    Goodchild, Anne

  • Performing Organizations:

    University of Washington, Seattle

    Civil and Environmental Engineering Department
    201 More Hall, Box 352700
    Seattle, WA  United States  98195-2700

    Oregon State University, Corvallis

    Department of Civil Engineering
    202 Apperson Hall
    Corvallis, OR  United States  97331-2302
  • Principal Investigators:

    Goodchild, Anne

    Hurwitz, David

    McCormack, Ed

  • Start Date: 20210316
  • Expected Completion Date: 20220315
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01784888
  • Record Type: Research project
  • Source Agency: Pacific Northwest Transportation Consortium
  • Contract Numbers: 69A3551747110
  • Files: UTC, RIP
  • Created Date: Oct 15 2021 1:59PM