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Open Mobility Foundation: SMART Grant Curb Collaborative

The Open Mobility Foundation’s SMART Curb Collaborative is a group of cities united in tackling challenges in curb management, reducing congestion, enhancing livability, and improving safety and equity on city streets. Each of these public agencies is a recipient of USDOT’s Strengthening Mobility and Revolutionizing Transportation (SMART) grant program, which provides funding to build data and technology capacity across the US.

In close coordination with the Open Mobility Foundation (OMF) Collaborative Program Manager, the UFL will support the nine cities of the SMART Grant collaborative as a component of joint services provided through enhanced membership with the OMF. The UFL will lead research initiatives within the Collaborative, contribute academic content and presentations to the group, and work closely with Cityfi and the OMF Collaborative team to support joint deliverables. The UFL will focus on three main thematic areas of inquiry to inform comparative learnings and insights across the Collaborative. The three themes are: curb infrastructure, curb policy, and curb demand.

Objectives

The Urban Freight Lab will:

  • Lead comparative analysis of the Collaborative across various indices (infrastructure, policy, and demand) and connect to questions around the digitization of curbspace
  • Support Cityfi and the OMF Program Manager by contributing expert academic and industry expertise to the Collaborative
  • Support the development of joint deliverables such as case studies.

Task 1. Project Management/Coordination with Collaborative and Support Team

Task 2. Organize and create a comparative rubric of Collaborative projects
The UFL, in collaboration with CityFi and OMF, will help to capture and document an overview of projects, catalog of research objectives and learnings, metrics and data to be collected by cities, and goals of projects. This will help to inform further comparative studies and learnings across the Collaborative.

Task 3. Curb Infrastructure
The UFL will document and compare the supply of curb infrastructure being studied by the nine Collaborative cities and gather publicly available data sources to be used for comparative analysis. The UFL will incorporate information collected in Task 2 such as information about the study area, curb inventory, and if data allows compare curb allocation between study areas.

Task 4. Curb Policy
The UFL will document and compare curb policies among cities. Once documented, researchers will create a typology of curb-related regulations, strategies and technologies adopted in the past and proposed in the SMART Cohort. Researchers will incorporate data collected from cities in Task 2 and undertake additional research and policy scan as needed.

Task 5. Curb Demand
The UFL team will use data collected in Task 2 to assess if any of the Cohort cities are capturing curb-use data. For cities where this data is available, the UFL team will estimate curb use for selected study areas within the cohort of cities and perform a comparative analysis. The accuracy of the analysis will depend on the availability of data provided by the selected cities.

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

Project Budget: $180,000 (UW amount: $80,000)

Lead Institution:

  • University of Washington, Urban Freight Lab (UFL)

Partner Institutions:

  • 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.

The simulation experience will be designed in a quarter-cab truck simulator at Oregon State University’s Driving and Bicycling Simulator Laboratory. Various simulation environments will be defined by changing road characteristics (such as land use, number of travel lanes, nearby signals, traffic in adjacent lanes), curb allocations (such as paid parking, commercial vehicle loading zones, and passenger load zones, as well as the size of the loading zones and their availability at the time of the vehicle arrival at the blockface), and other road users (passenger cars, ridehailing vehicles, bikes). 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 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.

The unique needs of delivery trucks and commercial vehicles are not acknowledged in current design practices. This study is intended to fill these gaps and serve as a valuable resource for policy makers, transportation engineers and urban planners.

A Data-Driven Simulation Tool for Dynamic Curb Planning and Management

Project Budget: $2.9M (UW amount: $500k)

Lead Institution:

  • Pacific Northwest National Lab (PNNL)

Partner Institutions:

  • Urban Freight Lab (UFL), University of Washington
  • Lawrence Berkeley National Laboratory (LBNL)
  • Lacuna Technologies, Inc. (Lacuna)
  • National Renewable Energy Laboratory (NREL)

Summary:

Curbs are a critical interfacing layer between movement and arrival in urban areas—the layer at which people and goods transition from travel to arrival—representing a primary point of resistance when joining and leaving the transportation network. Traditionally, curb spaces are statically supplied, priced, and zoned for specific usage (e.g., paid parking, commercial/passenger loading, or bus stops). In response to the growing demand for curb space, some cities are starting to be more intentional about defining curb usage. Examples of curb demand include not only traditional parking and delivery needs, but today include things like curb access requirements generated by micro delivery services, active transportation modes, and transportation network companies. And now due to the pandemic, increased demand comes from food/grocery pick-up/drop-off activities, as well as outdoor business use of curb space (e.g., outdoor restaurant seating).

Heightened demand and changing expectations for finite curb resources necessitates the implementation of new and dynamic curb management capabilities so that local decision-makers have the tools needed to improve occupancy and throughput while reducing the types of traffic disruptions that result from parking search and space maneuvering activities.

However, municipalities and cities currently lack tools that allow them to simulate the effectiveness of potential dynamic curb management policies to understand how the available control variables (e.g. price or curb space supply) can be modified to influence curb usage outcomes. On the other hand, transportation authorities and fleet managers lack the needed signage or communication platforms to effectively communicate the availability of curb space for a specified use, price, and time at scales beyond centralized lots and garages.

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).

Project Objectives:

Project objectives include the following:

  • Objective 1:  The team will develop a microscale curb simulation tool to model behavior of individual vehicles with different purposes at the curb along a blockface over time of day, accounting for price, supply, function, and maximum parking time.
  • Objective 2: The team will integrate the microscale simulation tool with the LBNL’s mesoscale (city-scale) traffic simulation tool, BEAM, for simulating traffic impacts of alternative curb management strategies and their effects on citywide and regional traffic, in terms of (1) travel time, (2) throughput (people and goods) into and out of urban centers, (3) reduced energy use and emissions (from parking search and congestion), and (4) curb space utilization.
  • Objective 3: The team will develop a dynamic curbspace allocation controller for various curb users, either municipal or commercial, for the purpose of a demonstration and pilot.
  • Objective 4: The team 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.
  • Objective 5: The team will perform demonstrations with stakeholder agencies and provide pathways to practice for promising curb allocation policies.
Presentation

Can Real-Time Curb Availability Information Improve Urban Delivery Efficiency?

 
Publication: 9th International Urban Freight Conference, Long Beach, May 2022
Publication Date: 2022
Summary:

Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affects such behavior.

In this study, we propose a simple method to quantitatively explore the parking cruising behavior of commercial vehicle drivers in urban areas using widely available GPS data, and how urban transport infrastructure impacts parking cruising times.

We apply the method to a sample of 2900 trips performed by a fleet of commercial vehicles, delivering and picking up parcels in downtown Seattle. We obtain an average estimated parking cruising time of 2.3 minutes per trip, contributing on average for 28 percent of total trip time. We also found that cruising for parking decreased as more curb-space was allocated to commercial vehicles load zones and paid parking and as more off-street parking areas were available at trip destinations, whereas it increased as more curb space was allocated to bus zone.

Recommended Citation:
Giacomo Dalla Chiara, Klaas Fiete Krutein, and Anne Goodchild (2022). Can Real-Time Curb Availability Information Improve Urban Delivery Efficiency? 9th International Urban Freight Conference (INUF), Long Beach, CA May 2022.