Research Projects
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- "Street design"
Start Date: January 2021
Funding: PacTrans (Region 10 University Transportation Center)
Project Budget: $180,000
Other PI(s): David Hurwitz (Oregon State University)
Summary:
This study will use a driving simulator to design a simulation experiment to test the behavior of commercial vehicle drivers under various parking and delivery situations and to analyze their reactions. The ability to modify the simulator’s environment will allow the researchers to relatively easily test a range of scenarios that correspond to different delivery and parking situations, such as changing road characteristics (land use, number of travel lanes, nearby signals, traffic in adjacent lanes), curb allocations (paid parking, commercial vehicle loading zones, passenger load zones), and other road users (passenger cars, ridehailing vehicles, bikes). In addition to monitoring behavior and decision-making, 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 commercial vehicle drivers, and will help city planners improve street designs and curb management policies to accommodate safe and efficient operations in a shared urban roadway environment. This study is intended to fill knowledge gaps and serve as a valuable resource for policy makers, transportation engineers, and urban planners.
This study will use a driving simulator to design a simulation experiment to test the behavior of commercial vehicle drivers under various parking and delivery situations and to analyze their reactions. The ability to modify the simulator’s environment will allow the researchers to relatively easily test a range of scenarios that correspond to different delivery and parking situations, such as changing road characteristics (land use, number of travel lanes, nearby signals, traffic in adjacent lanes), curb allocations (paid parking, commercial vehicle loading zones, passenger load zones), and other road users (passenger cars, ridehailing vehicles, bikes). In addition to monitoring behavior and decision-making, 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 commercial vehicle drivers, and will help city planners improve street designs and curb management policies to accommodate safe and efficient operations in a shared urban roadway environment. This study is intended to fill knowledge gaps and serve as a valuable resource for policy makers, transportation engineers, and urban planners.