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Urban Freight Lab

Research Topic: Curb Management

Curb management involves efficiently managing and regulating the use of curbside spaces along roadways. With the increasing demand for these spaces due to micromobility, transportation network companies (TNCs), ecommerce home delivery, and transit access, the goal is to inventory, optimize, allocate, and oversee curb space usage to maximize mobility, safety, and accessibility for users and needs.

Zero-Emission Zones: Turning Ideas into Action

C40 Cities, a consortium of cities worldwide with the collective goal of reducing greenhouse gas emissions, introduced an initiative in 2017 to create “Zero Emission Areas.” These areas, or zones, would be closed off to fossil fuel-burning vehicles and serve as a testbed for scaling up zero-emission regulation. Seattle, along with U.S. counterparts Austin, Texas and Los Angeles, CA, is a signatory to the Zero Emission Area Programme and as such, is obligated to create such an area by 2030.

Zero Emission Zones (ZEZ) can introduce obstacles to the urban freight and logistics industry. Though large delivery companies like Amazon, UPS, and FedEx are introducing electric vehicles (EVs), parcel and package delivery are not the only service included in the complex sector of urban freight. EVs are not yet widely available on the market and the high capital costs of introducing EVs into a company’s fleet can act as a barrier. However, there are strategies being tested and explored to reduce emissions including but not limited to zero emission curb zones, parcel lockers, e-cargo bikes, pricing strategies at the curb and at the point of sale (e.g. taxes and fees), consolidation centers, and other strategies. Additionally, many of these zones are being envisioned in areas with a focus on improving equity outcomes and across neighborhoods of different characteristics. However, no guidance exists for cities about how to approach the selection of these areas or tactics co-developed with the private sector.

Research Objectives

  • Develop a framework for evaluating geographic locations, existing policy tools, and key learning objectives or measures of success based on two different neighborhood typologies
  • Incorporate private sector stakeholders into the design process

Tasks

  • Task 1: Define the characteristics and goals of a zero-emission delivery zone
  • Task 2: Perform literature and policy scan on existing tools to push deliveries towards zero emission (industry and consumer-side)
  • Task 3: Identify 2 different neighborhood typologies in Seattle for analysis and define the study area boundaries
    • One neighborhood should meet existing definitions of a Justice 40 or equity focus area community as defined by City of Seattle (e.g. Georgetown)
    • One neighborhood should represent high-density demand for e-commerce and congestion (define?) (e.g. Capital Hill, South Lake Union)
  • Task 4: Collect publicly-available baseline data on neighborhood characteristics collect data (land use, types of businesses, demographics of residents)
  • Task 5: Develop potential scenarios, tactics, and metrics that reflect the unique characteristics of the chosen neighborhoods/typologies
    • The team will leverage existing relationships to perform private sector outreach, based on interviews: understand their priorities, reactions to scenarios under development.
  • Task 6: Recommendations and framework
    • How do you choose the site / site selection criteria and methodology
    • Tactics based on neighborhood typology characteristics- using policies available right now or with limited policy effort
    • Equity-Community metrics- How does the makeup of the zone/neighborhood impact tactics + metrics?
    • Key metrics- What are you trying to test and how will you measure?
    • Tools to accelerate the implementation of zero-emission deliveries.

Deliverable

Create a framework for zero emission zone design and case study of two different neighborhoods in Seattle.

Dataset

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

URL:  https://doi.org/10.7910/DVN/HVAUT3
Publication: Harvard Dataverse
Publication Date: 2023
Summary:

Three different data types were obtained from Oregon State Driving and Bicycling Simulator Laboratory for purpose of this report and they are as follow:

  1. Speed data consists of subject number, average speed, minimum speed, and all the independent variables. Speed data were collected based on the truck’s speed while driving through a certain scenario (out of 24). For each scenario, the average and minimum speed (mph) of 12 drivers were recorded along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals).
  2. Eye tracking data consists of subject number, total fixation duration (TFD) in milliseconds, area of interest (AOI), and all the independent variables. TFD data were collected while the truck driver maneuvers through a certain scenario (out of 24). For each scenario, the TFD for each AOI was recorded for 11 subjects along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals). AOI represent the area of interest that a driver fixates for a certain of time to generate the total fixation duration.
  3. Eye tracking data consists of subject number, GSR in peaks per minute, and all the independent variables. GSR data were collected while the truck driver maneuvers through a certain scenario (1 out of 24). For each scenario, the peaks per minute data was recorded for 11 subjects along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals). Peaks per minute represents the emotional arousal (i.e., something is scary, threating, joyful, etc.) that a driver generates when reacting to a particular event. Fourteen participants were recruited, two of them had a simulator sickness so they were excluded from the data and the analysis. While there are no quality or consistency issues with this data set, it should be noted that the sample is on the smaller side and that should be considered when interpreting derived results. The average values were calculated to apply robust statistical analysis for such data (speed and lateral position). As the experiment consists of 2x2x2x3 factorial design, each participant had to driver through 24 scenarios; therefore, 288 scenario observations were obtained and recorded in the excel file.
Authors: Dr. Anne GoodchildDr. Ed McCormackDr. Andisheh Ranjbari, David Hurwitz
Recommended Citation:
Goodchild, Anne; McCormack, Ed; Ranjbari, Andisheh; Hurwitz, David, 2023, "Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment", Harvard Dataverse. https://doi.org/10.7910/DVN/HVAUT3.
Paper

Simulation-Based Analysis of Different Curb Space Type Allocations on Curb Performance

 
Download PDF  (3.49 MB)
URL: https://doi.org/10.1080/21680566.2023.2212324
Publication: Transportmetrica B: Transport Dynamics
Volume: 11 (1)
Pages: 1384-1405
Publication Date: 2023
Summary:

Curbspace is a limited resource in urban areas. Delivery, ridehailing and passenger vehicles must compete for spaces at the curb. Cities are increasingly adjusting curb rules and allocating curb spaces for uses other than short-term paid parking, yet they lack the tools or data needed to make informed decisions. In this research, we analyze and quantify the impacts of different curb use allocations on curb performance through simulation. Three metrics are developed to evaluate the performance of the curb, covering productivity and accessibility of passengers and goods, and CO2 emissions. The metrics are calculated for each scenario across a range of input parameters (traffic volume, parking rate, vehicle dwell time, and street design speed) and compared to a baseline scenario. This work can inform policy decisions by providing municipalities tools to analyze various curb management strategies and choose the ones that produce results more in line with their policy goals.

Authors: Thomas MaxnerDr. Andisheh RanjbariŞeyma Güneş, Chase Dowling (Pacific Northwest National Laboratory)
Recommended Citation:
Thomas Maxner, Andisheh Ranjbari, Chase P. Dowling & Şeyma Güneş (2023) Simulation-based analysis of different curb space type allocations on curb performance, Transportmetrica B: Transport Dynamics, 11:1, 1384-1405, DOI: 10.1080/21680566.2023.2212324

Revenue-Related Strategies for New Mobility Options

The Urban Freight Lab (UFL) is partnering with ECONorthwest and Cityfi to develop a research product for the National Cooperative Highway Research Program (NCHRP) on the topic of revenue strategies for new mobility options. The team will analyze the public sector’s potential role in using revenue-related strategies to encourage or discourage new mobility options in personal mobility and goods delivery.

Transportation services often operate in publicly owned and publicly managed spaces, make use of public rights-of-way, and produce mobility benefits for a broad array of users. The public sector is responsible for managing and pricing those rights-of-way and delivering services in an equitable way. Recovering the public costs of management and provisioning from private transportation services and their users is essential for maintaining public benefit. And sometimes the public sector needs to help private services to thrive.

The research methodology for this project is designed to be iterative: activities and research will build on previous research and activities. We will begin with the development of a revenue framework informed by a broad review of the literature, a policy scan, and workshop sessions with transportation and other public agency representatives that regulate and collect revenue from new mobility services. The framework will include revenue-related strategies based on:

    • (a) identifiable need
    • (b) nexus to cost responsibility
    • (c) policy outcome
    • (d) other factors such as access to technology and ease of administration.

We will then take a deeper dive into each personal mobility mode and goods delivery market segment to apply the framework. We will also provide examples to illustrate the opportunities and challenges of a variety of revenue strategies. We will also conduct additional workshops with public agency representatives, industry representatives, and other transportation stakeholders. Finally, we will create a spreadsheet-based Revenue Calculator that allows interested individuals to estimate how much revenue could be generated using different assumptions and strategies. The work will culminate with the development of a Toolkit that will be submitted to NCHRP and made available for wider distribution.

Objectives

The objective of this research is to develop a toolkit for transportation agencies that addresses how revenue-related strategies (e.g., taxes, fees, and subsidies) support policy objectives and shape the deployment of new mobility options. The toolkit will assist agencies to develop, evaluate, implement, and administer revenue-related strategies for new mobility options that transport people and goods.

The research will include:

  1. New and evolving transportation options for people and goods that interact with the existing built environment and travel throughout an area
  2. Incentives and disincentives that result from revenue-related strategies
  3. Policy implications of revenue-related strategies for new mobility options including revenue potential, mobility, travel demand, safety, equity, environment, economic development, infrastructure design, operations, and maintenance
  4. Mechanisms for revenue collection and distribution for different mobility options in different scenarios
  5. The ease and difficulty of implementing and enforcing different revenue-related strategies for new mobility options
  6. Potential roles and responsibilities of governmental organizations and private entities

Last-Mile Freight Curb Access: Digitizing the Last Mile of Urban Goods to Improve Curb Access and Use

The U.S. Department of Transportation (USDOT) awarded a $2 million grant under its SMART (Strengthening Mobility and Revolutionizing Transportation) grant program to support the development of the Last-Mile Freight Curb Access Program: Digitizing the Last Mile of Urban Goods to Improve Curb Access and Utilization, a collaboration between the Urban Freight Lab, Seattle Department of Transportation, and Open Mobility Foundation. This project will develop sensor-based technology solutions that address to transportation problems, enabling commercial vehicles to make faster, safer, and more efficient deliveries with reduced vehicle emissions.

The Last Mile Freight Curb Access Program focuses on providing commercial vehicle drivers with real-time information to park legally and expedite deliveries. Research from a 2019 Urban Freight Lab study showed that more than 40% of commercial vehicles in downtown Seattle park in unauthorized locations. Another study showed that equipping commercial vehicles with real-time parking availability and load zone information could reduce their “cruising” time by nearly 30%. The project aims to make information about curbside regulations digitized and more accessible to commercial drivers, and leverage this data to improve regulations.

Other cities including Portland, San Francisco, San Jose, Los Angeles, Minneapolis, Philadelphia, and Miami-Dade County have also received SMART grants to implement similar technology-based solutions for improving curb access.

Background

Since 2010, the Seattle Department of Transportation (SDOT) has been a national leader in data-driven curbside management by using parking occupancy data to set on-street parking rates. We proposed to extend our data-driven pricing and curb literacy to a new use: designated commercial vehicle load zones (CVLZ) and the commercial vehicle permit (CVP). Our plan is to establish new CVP policies in close collaboration with urban freight companies, adjacent businesses, and other critical stakeholders; implement a digital CVP built on the Curb Data Specification (CDS) that enables capture of curb utilization measurements and communicates demand management policies; and transform our legacy digital curb inventory to the national CDS standard.

Strategies

To address these challenges, SDOT proposes a SMART project that will use a combination of digital technologies coupled with targeted outreach. This approach will be implemented through three key strategies:

  1. Engage with local businesses and urban freight companies to understand challenges and build a foundation of trust SDOT will engage with a variety of stakeholders including local neighborhood businesses, commercial vehicle users from large carriers, and commercial vehicle permit (CVP) holders from small and local businesses. The goal is to build trust and work collaboratively with our users to modernize and improve our existing CVP to create a system that works for urban freight companies, local businesses, and benefits the community at large.
  2. Prototype a digital CVP and use findings to modernize and scale the system SDOT will conduct a vendor procurement to prototype and assess a wireless vehicle-to-curb infrastructure (V2I) communication system, built on top of the Curb Data Specification (CDS) standard as a new way to manage our CVP. Data collected through this prototype will be leveraged by the UFL to conduct research to develop standardized data collection efforts for commercial curb use and create new data-driven policy and permit recommendations.
  3. Collaborate with a national cohort of cities implementing the Curb Data Specification SDOT will partner with the Open Mobility Foundation (OMF) and collaborate with a national cohort of OMF member cities to support the shared objectives in how CDS can help cities and companies pilot and scale dynamic curb use. SDOT will share lessons from Stage 1 prototyping with OMF cohort cities to strengthen all CDS-related SMART grant projects and better position proven technologies to be implemented at scale for a Stage 2 project. SDOT is uniquely positioned to deliver a successful Stage 1 project focusing on commercial vehicle curb access and utilization given our existing CVP and leadership in data driven curbside management. Specifically, this project will directly address the SMART goals of equity and access, partnerships, and integration and build the foundation for dramatic improvements in safety, reliability, and climate in Stage 2. Our goal is that the Stage 1 learnings will allow us to scale a digital CVP for citywide adoption in Stage 2, thus promoting interoperability of technology solutions to improve curb access for commercial curb users citywide. Our approach centers on stakeholder and community partnerships, data-driven assessment, and technical capacity-building. Potential outcomes for testing and implementation in Stage 2 include updated policies or curb allocations that might address inequities through deeper understanding of the variety of commercial users of the curb, reduced carbon emissions by creating or incenting CV zero emission zones, and decreased impacts to vulnerable road users through optimized curb allocation.

Objectives

The expected benefits of Stage 1 will be threefold:

    1. Rigorously assess the piloted technology system to understand its scaling potential: The project will develop a technology assessment methodology that will look critically at accuracy and data use model development. This assessment will be transparent and developed in collaboration with OMF cohort cities to ensure solutions are scalable while meeting the core needs of Seattle’s digital CVP.
    2. Create a CDS framework for standardizing data collection efforts of commercial curb space: SDOT will share lessons learned from Stage 1 prototyping and policy recommendations with OMF cohort cities to collectively strengthen all CDS-related SMART grant projects and better position proven technologies to be implemented at scale.
    3. Create new data-driven commercial vehicle policy and permit recommendations to be enacted during Stage 2 of this grant

The recommendations will be informed by data models created by the UFL using utilization data from the project overlayed with characteristics of adjacent urban form and land use. These models will help SDOT identify areas for adjustments to existing curb allocation as well as establish a deeper understanding of the variety of commercial vehicle user behavior at the curb to meet climate goals. We anticipate these policies will benefit both curb users and local community members by reducing congestion and creating safer streets.

Chapter

Success Factors for Urban Logistics Pilot Studies

URL: https://www.taylorfrancis.com/chapters/edit/10.4324/9781003241478-27/success-factors-urban-logistics-pilot-studies-andisheh-ranjbari-anne-goodchild-elizabeth-guzy
Publication: The Routledge Handbook of Urban Logistics
Publication Date: 2023
Summary:

The last mile of delivery is undergoing major changes, experiencing new demand and new challenges. The rise in urban deliveries amid the societal impacts of the COVID-19 pandemic has dramatically affected urban logistics. The level of understanding is increasing as cities and companies pilot strategies that pave the way for efficient urban freight practices. Parcel lockers, for instance, have been shown to reduce delivery dwell times with such success that Denmark increased its pilot program of 2,000 lockers to 10,000 over the past two years. This chapter focuses on challenges faced during those pilots from technical, managerial and operational perspectives, and offers examples and lessons learned for those who are planning to design and/or run future pilot tests. On-site management proved to be critical for locker operations.

Authors: Dr. Andisheh RanjbariDr. Anne GoodchildElizabeth Guzy
Recommended Citation:
Ranjbari, Andisheh & Goodchild, A & Guzy, E. (2023). Success Factors for Urban Logistics Pilot Studies. 10.4324/9781003241478-27.

Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

The Urban Freight Lab (UFL) received $1.5 million in funding from the U.S. Department of Energy to help goods delivery drivers find a place to park without driving around the block in crowded cities for hours, wasting time and fuel and adding to congestion. The project partners will integrate sensor technologies, develop data platforms to process large data streams, and publish a prototype app to let delivery drivers know when a parking space is open – and when it’s predicted to be open so they can plan to arrive when another truck is leaving.

The UFL will also pilot test common carrier locker systems in public and private load/unload spaces near transit stops. Transit riders, downtown workers, and residents will be able to pick up packages they ordered online from any retailer in a convenient and secure locker in a public plaza or outside their office. The benefits don’t stop there. Common carrier lockers create delivery density that increases the productivity of parking spaces and provides significant commercial efficiencies. They do this by reducing the amount of time it takes delivery people to complete their work. The driver parks next to the locker system, loads packages into it, and returns to the truck. When delivery people spend less time going door-to-door, it decreases the time their truck needs to be parked, increasing turnover and adding parking capacity in crowded cities.

This is a timely project as cities are looking for new strategies to accommodate the rapid growth of e-commerce. Online shopping has grown by 15% annually for the past 11 years, and is now 9% of total retail sales in the U.S., with $453.5 billion in revenue in 2017. Many online shoppers want the goods delivery system to bring them whatever they want, where they want it, in one to two hours. At the same time, many cities are replacing goods delivery load/unload spaces with transit and bike lanes. Cities need new load/unload space concepts supported by technology to make the leap to autonomous cars and trucks in the street, and autonomous freight vehicles in the Final 50 Feet of the goods delivery system. The Final 50 feet segment starts when a truck parks in a load/unload space, and includes delivery persons’ activities as they maneuver goods along sidewalks and into urban towers to make their deliveries.

The goals of this project are to:

  • Reduce parking seeking behavior by 20% in the pilot test area by returning current and predicted load/unload space occupancy information to users on a web-based and/or mobile platform to inform real-time parking decisions.
  • Reduce parcel truck dwell time in pilot test area locations by 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems.
  • Increase network and commercial firms’ efficiency by increasing curb and alley space occupancy rates, and underutilized private loading bay occupancy in the p.m. peak, in the pilot test area.

Cost-share partnering organizations are:

  • Seattle Department of Transportation
  • Bellevue Department of Transportation
  • CBRE Seattle
  • King County Metro Transit
  • Kroger Company
  • Puget Sound Clean Air Agency
  • Sound Transit

Members of the UFL are also participating in the project. Pacific National National Laboratory (PNNL) is a partner, completing several of the project tasks.

Technical Report

Transit Corridor Study

 
Download PDF  (6.60 MB)
URL: https://mic.comotion.uw.edu/wp-content/uploads/2021/07/TransitCorridorStudy_InterimReport_June2021.pdf
Publication Date: 2021
Summary:

This study is sponsored by Amazon, Bellevue Transportation department, Challenge Seattle, King County Metro, Seattle Department of Transportation, Sound Transit, and Uber, with support from the Mobility Innovation Center at UW CoMotion.

Population and extended economic growth in many Seattle neighborhoods are driving increased demand for private car travel along with transportation services such as ridehailing and on-demand delivery. Together, these trends are adding to existing demand for loading and unloading operations throughout the city, and exacerbating traffic congestion. Anecdotal evidence indicates that passenger/delivery vehicle stops at or next to transit stops can interfere with bus operations, causing longer or more volatile delays. The increased travel times and reduced reliability further erode the attractiveness of transit to travelers. Thus, it is important to understand how transit, ridehailing, and goods delivery vehicles interact in terms of both operations and travel demand.
This project focuses on the analysis of open-source transit data to screen for locations with slow and/or unreliable bus travel times, and couple that data with interference observation, environmental, and traffic-related data to potentially predict the likely causes. We have developed tools to identify transit corridors with high levels of interference from other road users, including passenger cars, ridehailing vehicles and goods delivery vehicles. These tools are applied to transit corridors in Seattle and Bellevue, and methods have been developed to identify likely sources of interference from available data.
We drew on multiple data sources for identifying high-interference corridors in the region, including:
  • a virtual workshop with participants from beneficiary agencies and stakeholders to solicit input;
  • an online crowdsourcing survey to engage the community and gather feedback from all road users;
  • route-level ridership data from King County Metro; and
  • aggregated pick-up/drop-off data on ridehailing activities from SharedStreets.
Data was consolidated and 10 corridors were selected based on their likelihood of containing interference between buses and other road users, transit ridership levels, and stakeholder and community feedback.
In addition, we have developed a tool for identifying corridors with slow and/or unreliable bus travel times from open-source real-time transit data. We implemented a pipeline for ingesting and analyzing King County Metro’s real-time Generalized Transit Feed Specification data (GTFS-RT) at 10-second intervals. Using this pipeline, active bus coordinate and schedule adherence data has been scraped and stored to an Amazon Web Services (AWS) server since September 2020. We developed efficient methods to aggregate tracked bus locations and assign them to roadway segments, and quantified delays in terms of schedule deviation and ratio of median to free-flow speeds, among other metrics. We have developed a web based visualization tool to display this data, and it is being updated daily with aggregated performance metrics from our database.
To collect ground truth validation data along selected corridors, we implemented an online data collection tool for field observations, and recruited research assistants to observe bus operations along the study corridors and record information on bus traversals and instances of interference. This dataset is analyzed alongside the GTFS-RT data, environmental, and traffic related data to identify instances of delay and predict the likely causes.
Field data was collected for three weeks along eight of the selected corridors in March 2021, but was later paused due to depressed levels of transportation activity during the COVID-19 pandemic and the current unstable condition of travel choices and city traffic (and thus interferences). Preliminary analysis on the collected data revealed that there is not a substantial effect shown in the GTFS-RT data when a bus is interfered with; however, there were not a lot of interference observations in the collected field data. So, it remains to be seen whether the lack of an identifiable effect is due to the lack of ground truth data, lack of precision in the automatic vehicle location system, or the relatively low impact of an interference when compared to the effects of general traffic congestion, signals, and other roadway conditions. A linear regression model was also generated to determine the extent to which roadway characteristics can predict segment performance, which produced mildly predictive results.
As businesses and transit services continue to reopen, there will likely be an increase in the amount of transit interference experienced between buses and other roadway users, which will potentially allow for the gathering of more ground truth validation data. Field observations will resume in late Summer/early Fall 2021 and will continue until enough data is collected to either (1) model connections between observed interference and bus delays in the GTFS-RT data; or (2) determine whether significant delays cannot be linked to observed instances of interference in the study corridors. The GTFS-RT data scraping will continue daily, and summarized in the developed interactive visualization tool.
The major anticipated benefits of the project can be summarized as follows:
  • This work will help identify network-wide road and route segments with slow and/or unreliable bus travel times. We may also be able to identify main causes of delay in the study corridors.
  • Moreover, we expect that this work will generate reusable analytical tools that can be applied by local agencies on an ongoing basis, and by other researchers and transportation agencies in their own jurisdictions.
  • The outcomes of this work will enable identifying corridors with slow and/or unreliable bus travel times as candidates for specific countermeasures to increase transit performance, such as increased enforcement, modified curb use rules, or preferential bus or street use treatments. Targeting such countermeasures towards priority locations will result in faster and more reliable bus operations, and a more efficient transportation network at a lower cost to transit agencies.
Authors: Dr. Andisheh Ranjbari, Zack Aemmer, Borna Arabkhedri, Don MacKenzie
Paper

An Empirical Analysis of Passenger Vehicle Dwell Time and Curb Management Strategies for Ride-Hailing Pick-Up/Drop-Off Operations

URL: https://doi.org/10.1007/s11116-023-10380-6
Publication: Transportation
Publication Date: 2023
Summary:

With the dramatic and recent growth in demand for curbside pick-up and drop-off by ride-hailing services, as well as online shopping and associated deliveries, balancing the needs of roadway users is increasingly critical. Local governments lack tools to evaluate the impacts of curb management strategies that prioritize different users’ needs. The dwell time of passenger vehicles picking up/dropping off (PUDO) passengers, including ride-hailing vehicles, taxis, and other cars, is a vital metric for curb management, but little is understood about the key factors that affect it. This research used a hazard-based duration modeling approach to describe the PUDO dwell times of over 6,000 passenger vehicles conducted in Seattle, Wash. Additionally, a before-after study approach was used to assess the effects of two curb management strategies: adding PUDO zones and geofencing. Results showed that the number of passenger maneuvers, location and time of day, and traffic and operation management factors significantly affected PUDO dwell times. PUDO operations took longer with more passengers, pick-ups (as opposed to drop-offs), vehicle´s trunk access, curbside stops, and in the afternoon. More vehicles at the curb and in adjacent travel lanes were found to be related to shorter PUDO dwell times but with a less practical significance. Ride-hailing vehicles tended to spend less time conducting PUDOs than other passenger vehicles and taxis. Adding PUDO zones, together with geofencing, was found to be related to faster PUDO operations at the curb. Suggestions are made for the future design of curb management strategies to accommodate ride-hailing operations.

Authors: José Luis Machado LeónDr. Anne Goodchild, Don MacKenzie (University of Washington College of Engineering)
Recommended Citation:
Machado-León, J.L., MacKenzie, D. & Goodchild, A. An Empirical Analysis of Passenger Vehicle Dwell Time and Curb Management Strategies for Ride-Hailing Pick-Up/Drop-Off Operations. Transportation (2023). https://doi.org/10.1007/s11116-023-10380-6
Paper

Curbspace Management Challenges and Opportunities from Public and Private Sector Perspectives

 
Download PDF  (1.28 MB)
URL: https://journals.sagepub.com/doi/10.1177/03611981211027156
Publication: Transportation Research Record: Journal of the Transportation Research Board
Publication Date: 2021
Summary:

Through structured interviews with public agency and private company staff and a review of existing pilot project evaluations and curb management guidelines, this study surveys contemporary approaches to curb space management in 14 U.S. cities and documents the challenges and opportunities associated with them. A total of 17 public agencies (including public works departments, transportation agencies, and metropolitan planning organizations) in every census region of the U.S. and 10 technology companies were interviewed.

The results show that the top curb management concerns among public officials are enforcement and communication, data collection and management, and interagency coordination. Interviewees reported success with policies such as allocating zones for passenger pick-ups and drop-offs, incentives for off-peak delivery, and requiring data sharing in exchange for reservable or additional curb spaces. Technology company representatives discussed new tools and technologies for curb management, including smart parking reservation systems, occupancy sensors and cameras, and automated enforcement. Both public and private sector staff expressed a desire for citywide policy goals around curb management, more consistent curb regulations across jurisdictions, and a common data standard for encoding curb information.

Authors: Caleb DiehlDr. Andisheh RanjbariDr. Anne Goodchild
Recommended Citation:
Diehl, C., Ranjbari, A., & Goodchild, A. (2021). Curbspace Management Challenges and Opportunities from Public and Private Sector Perspectives. Transportation Research Record. https://doi.org/10.1177/03611981211027156