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 will build upon a previous Urban Freight Lab study (funded by the U.S. Department of Energy) that was aimed at improving commercial vehicle delivery efficiency generating and providing real-time and future parking information to delivery drivers. In this subsequent study, researchers will build upon the knowledge developed and the existing network of parking occupancy sensors installed in a 10-block study area in the Belltown neighborhood of Seattle, Washington, to explore how historical parking occupancy data can be used by urban planners and policymakers to better allocate curb space to commercial vehicles. We will use data from the sensor network and explore the relationship between the built environment (location and characteristics of establishments and urban form) and the resulting occupancy patterns of commercial vehicle load zones and passenger load zones in the study area.
This project is a continuation of the West Seattle Bridge Case Study Phase I.
West Seattle (WS) is an area of the city of Seattle, Washington, located on a peninsula west of the Duwamish waterway and east of the Puget Sound. In March 2020, the West Seattle High Bridge (WSHB), the main bridge connecting WS to the rest of the city, was closed to traffic due to its increasing rate of structural deterioration.
The Urban Freight in 2030 project will explore emerging urban freight trends, their impacts on local and global sustainable development, and propose Urban Freight Lab’s future course of action. We'll use the expertise of the Urban Freight Lab members and partners, supported by up-to-date research and subject specialists, to create a shared vision of the future of urban delivery in 2030, and produce vision documents to be shared publicly, outlining and detailing the Urban Freight Lab’s vision of the future of urban freight.
For this project, two research groups at the University of Washington (the Urban Freight Lab and Lilian Ratliff's research group) will collaborate to integrate different data streams currently being collected separately and in an uncoordinated way, including data from in-ground curb sensors at CVLZs and PLZs, paid parking transactions at paid parking spaces, and data obtained from timelapse camera recordings. The groups will create a holistic framework to analyze not only the curb behaviors of different users but also how different users interact in the competition for limited curb space. The collaboration will advance the state of environmental science by providing the most complete dataset and creating innovative tools to inform policymaking on curb parking pricing and curb allocation to reduce cruising for parking and unauthorized parking events, therefore tackling the climate crisis by reducing urban vehicle emissions and traffic congestion, and the state of data science by developing a new statistical framework and machine learning algorithms to analyze curb space use behaviors from users and develop recommendations for cities on how to better allocate curb space to different competing demands.