Research Projects
Searching for:
- "Curb allocation"
Demand for curb space is increasing with the rise of TNCs, micro-delivery services, electric vehicle infrastructure, micromobility, and complete streets. Municipalities are addressing these challenges with new curb allocation policies that improve flow and reduce traffic disruption.
Start Date: May 2024
Funding: U.S. Department of Transportation (USDOT) SMART (Strengthening Mobility and Revolutionizing Transportation) Grants Program
Project Budget: $100,000
Principal Investigator(s): Dr. Anne Goodchild
Summary:
The UFL will lead research initiatives within the Open Mobility Foundation’s SMART Curb Collaborative, contribute academic content and presentations to the group, and work closely with Cityfi and the Collaborative to support joint deliverables. The UFL will focus on three main thematic areas of inquiry to inform comparative learnings and insights across the Collaborative: curb infrastructure, curb policy, and curb demand.
The UFL will lead research initiatives within the Open Mobility Foundation’s SMART Curb Collaborative, contribute academic content and presentations to the group, and work closely with Cityfi and the Collaborative to support joint deliverables. The UFL will focus on three main thematic areas of inquiry to inform comparative learnings and insights across the Collaborative: curb infrastructure, curb policy, and curb demand.
Start Date: January 2021
Funding: Pacific Northwest Transportation Consortium (PacTrans)
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.
Start Date: January 2020
Funding: U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO)
Project Budget: $500,000
Summary:
This project aims to develop a city-scale dynamic curb use simulation tool and an open-source curb management platform. The envisioned simulation and management capabilities will include dynamically and concurrently controlling price, number of spaces, allowed parking duration, time of use or reservation, and curb space use type (e.g., dynamic curb space rezoning based on supply and demand). Researchers will design, implement, and test a curbside resource usage platform for fleet vehicles communications at commercial vehicle load zones (CVLZs), passenger load zones (PLZs), and transit stops, and perform demonstrations with stakeholder agencies and provide pathways to practice for promising curb allocation policies.
This project aims to develop a city-scale dynamic curb use simulation tool and an open-source curb management platform. The envisioned simulation and management capabilities will include dynamically and concurrently controlling price, number of spaces, allowed parking duration, time of use or reservation, and curb space use type (e.g., dynamic curb space rezoning based on supply and demand). Researchers will design, implement, and test a curbside resource usage platform for fleet vehicles communications at commercial vehicle load zones (CVLZs), passenger load zones (PLZs), and transit stops, and perform demonstrations with stakeholder agencies and provide pathways to practice for promising curb allocation policies.