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Dataset

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

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.
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

 
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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

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.

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

A Holistic Data-Driven Framework for Curb Space Use and Policy-Making

The curb space is the portion of the public rights-of-way that demarcates the roadway from the sidewalk, separating pedestrian flow from moving vehicles. It is a scarce public resource that has been traditionally used for storing private passenger vehicles. However, the past decade has seen not only a surge in demand but also the rise of new demands for curb space, driven by new forces of change: the rise in online shopping has driven up the demand for delivery vehicle loading and unloading spaces; the increasing use of ride-hailing vehicles such as Uber and Lyft has exacerbated curb space congestion; the rapid adoption of micromobility modes has increased their parking demand, among others. The pandemic has only exacerbated the issue due to greater demand for home delivery services and novel use cases such as curbside cafes.

The mismatch between the increase in demand and the lack of curb space supply represents a bottleneck in the urban transportation system, increasing the cruising for parking time — the time drivers spend searching for parking — as well as the occurrence of unauthorized parking. Both consequences heavily impact urban traffic congestion, increasing emissions and lowering the quality of life for urban dwellers, as well as can potentially create unsafe conditions. More broadly, the curb is a major linchpin in city operations: beyond congestion, it also affects business district vitality, residential access, and even policy decisions about new constructions.

To address these challenges, cities need greater access to data science and machine learning tools to have better insights into the overall use of and demand for curb space, with the final objective to be able to effectively manage the limited amount of curb space available. This includes the need for tools to aid in optimizing pricing mechanisms and to adaptively learn the most efficient and sustainable allocation of space to the different types of users.

Two research groups at the University of Washington have taken different but complementary approaches to study the curb and build tools to help cities understand different curb demands and better manage the limited curb space available.

The Urban Freight Lab, led by Prof. Anne Goodchild, approaches the study of the curb from the perspective of commercial vehicles, including delivery and ridehailing vehicles. The group has collected data and derived statistical models of curb users’ behaviors for commercial vehicles. Furthermore, the group has piloted on-the-ground technologies and policies to improve curb access. In a recent project, Prof. Goodchild’s group deployed 300 in-ground occupancy sensors at commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) — curb spaces dedicated to commercial and ridehailing vehicles — in a 10-block study area in the Belltown neighborhood of Seattle, WA, collecting more than a year of fine-grained curb-use data.

The research group led by Prof. Lilian Ratliff approaches the study of the curb primarily from the perspective of private passenger vehicles, applying innovative machine learning and game theory tools to study curb management policies. In a recent project, Prof. Ratliff’s group developed a new modeling framework to estimate on-street paid parking occupancies — spaces dedicated for private passenger vehicle parking — from parking transaction data and sparse ground truth occupancy data obtained via manual counts and timelapse camera images.

The research in Goodchild’s and Ratliff’s groups has been impactful. Yet, load zone and paid parking curb-uses are highly interdependent given that the zones dedicated to the different use cases are often on the same curb. Hence, a more holistic approach to learning curb use behaviors is needed in order to effectively manage the whole curb.

For this project, the two groups 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. With such a complete dataset, 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 proposed collaboration will advance the state of the art in environmental sciences 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.

The proposed collaboration will also advance the state of the art in data science by developing a new statistical framework and machine learning algorithms to analyze curb space use behaviors from different curb space users and develop much-needed recommendations for cities on how to better allocate curb space to different competing demands.

The project will have a direct impact on the City of Seattle as both groups are currently collaborating with the Seattle Department of Transportation to create a more data-driven decision-making framework for curb space policies, as well as an impact on the fields of urban transportation and logistics by merging two separate kinds of literature, the more traditional transport theory taking private passenger vehicles as the main actor in urban transportation and the urban logistics field that focuses on commercial vehicles operations in urban areas.

Concrete outcomes of the projects obtained during the year of collaboration will include a joint seminar series of the two groups, presenting their ongoing research projects that focused on the curb, a join effort to collect data in Seattle, and integrating data streams to generate a complete dataset of curb use for the Seattle downtown area. Additionally, the groups will jointly write a scientific paper proposing a holistic framework to analyze the curb from the different users’ perspectives. The proposed collaboration will expand upon the projects Prof. Goodchild’s and Prof. Ratliff’s groups are currently working on, and develop a new set of data and tools that will enable future joint grant proposals by the two groups.

Paper

How to Improve Urban Delivery Routes’ Efficiency Considering Cruising for Parking Delays

 
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Publication Date: 2022
Summary:

This paper explores the value of providing parking availability data in urban environments for commercial vehicle deliveries. The research investigated how historic cruising and parking delay data can be leveraged to improve the routes of carriers in urban environments to increase cost efficiency. To do so, the research developed a methodology consisting of a travel time prediction model and a routing model to account for parking delay estimates. The method was applied both to a real-world case study to show its immediate application potential and to a synthetic data set to identify environments and route characteristics that would most benefit from considering this information.

Results from the real-world data set showed a mean total drive time savings of 1.5 percent. The synthetic data set showed a potential mean total drive time savings of 21.6 percent, with routes with fewer stops, a homogeneous spatial distribution, and a higher cruising time standard deviation showing the largest savings potential at up to 62.3 percent. The results demonstrated that higher visibility of curb activity for commercial vehicles can reduce time per vehicle spent in urban environments, which can decrease the impact on congestion and space use in cities.

Authors: Fiete KruteinDr. Giacomo Dalla ChiaraDr. Anne Goodchild, Todor Dimitrov (University of Washington Paul G. Allen School of Computer Science & Engineering)
Recommended Citation:
Krutein, Klaas Fiete and Dalla Chiara, Giacomo and Dimitrov, Todor and Goodchild, Anne, How to Improve Urban Delivery Routes' Efficiency Considering Cruising for Parking Delays. http://dx.doi.org/10.2139/ssrn.4183322
Paper

Understanding the Use of the Curb Space and Alley for Unloading and Loading Operations: A Seattle Case Study

 
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Publication: VREF: Current Issues Influencing Urban Freight Research
Publication Date: 2018
Summary:

Purpose: The increasing growth of e-commerce has been putting pressure on local governments to rethink how they manage street curb parking and alley operations for trucks and other delivery vehicles. Many studies state that the competition for space among road users and the lack of adequate infrastructure force delivery drivers to either search for vacant spaces or park in unsuitable areas; which negatively impacts road capacity and causes inconvenience to other users of the road (Butrina et al. (2017); Dablanc & Beziat (2015); Aiura & Taniguchi (2005)).

However, local governments often lack data about the current usage of the parking infrastructure, which is necessary to make well-informed decisions regarding freight planning, especially in dense, constrained urban areas.

For these reasons, the purpose of this research is to address the lack of information regarding the usage of the infrastructure at the public right of way used for freight and parcel load and unload operations.

Research Approach:  The approach of this research is quantitative. The SCTL research team developed two independent data collection replicable methods to quantify the usage of (i) curb spaces and (ii) alleys in selected areas of Seattle’s One Center City.

Findings and Originality: This research presents two case studies for selected areas in Seattle’s One Center City area. The first one documents and analyzes the duration and types of curb spaces used by delivery vehicles in the surrounding area of five prototype buildings. We also considered all vehicles occupying on-street commercial vehicle load zones located in the study area. The second case study conducts an alley occupancy survey, looking into all parking activities (including trucks, vans, garbage collection vehicles, and passenger vehicles) in seven alleys. A total of twelve survey locations were monitored during 2-3 weekdays and 4-8 hours per day.

Research Impact: This research provides practical step-by-step methods to conduct occupancy studies of public parking for loading and loading operations, which helps to understand the current usage of a key piece of the infrastructure network. The results provide critical information to make well informed decisions regarding urban freight planning especially in dense, constrained urban areas.

Practical Impact :This research describes the steps required to develop an efficient and systematic data collection method to build a database that will provide evidence-based learning to Seattle local officials. By applying these quantitative methods, we provided decision support to pilot-test and potentially adopt solutions to improve the freight parking infrastructure performance.

Recommended Citation:
Giron-Valderrama, G., Machado-León, J. L., & Goodchild, A. Understanding the use of the curb space and alley for unloading and loading operations: A Seattle Case Study. VREF: Current Issues Influencing Urban Freight Research, 37.

Analysis of Parking Utilization Using Curb Parking Sensors (Task Order 10)

In a Department of Energy-funded project led by the Urban Freight Lab, a network of parking occupancy sensors was installed in a 10-block study area in the Belltown neighborhood of Seattle, Washington. The study aimed at improving commercial vehicle delivery efficiency generating and providing real-time and future parking information to delivery drivers and carriers. This project will build upon the existing sensor network and the knowledge developed to explore how historical parking occupancy data can be used by urban planners and policymakers to better allocate curb space to commercial vehicles. The proposed project will use data from the existing 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.

Task 1 – Gather public data sources

Using public data sources (e.g. SDOT open data portal and Google Maps Places) the research team will obtain data on buildings and business establishments located in the Belltown study area (1st to 3rd Ave and Battery to Stewart Street). Data will include the location of business establishments, building height, land use, and estimates of the number of residents per building.

Task 2 – Analyze sensor data and estimate parking events

The research team will retrieve and process 1-year historical sensor data from the sensor network deployed in the study area. Sensor data is not directly usable as sensors are placed every 10 feet and a vehicle parking in a curb space might activate more than one sensor. Therefore, the research team will develop an algorithm that takes as input raw sensor data and gives as output estimate individual parking events, each consisting of a start time, curb space, and parking dwell time. The algorithm will be validated and algorithm performance will be reported.

Task 3 – Estimate parking utilization for each curb parking space

Using the estimated parking events obtained from task 2, the research team will analyze parking patterns and estimate total parking utilization for each curb parking space over time.

Task 4 – Design and perform an establishment survey

The research team will design an establishments survey to gather data on opening times, number of employees, type of business, and number of trips generated by business establishments in the study area. The survey will then be deployed and data will be collected for the business establishments in the study area. Descriptive statistics will be obtained characterizing the demand of freight trips generated by business type in the study area.

Task 5 – Analysis of parking utilization

The research team will perform statistical modeling to understand factors affecting curb space utilization in relationship with the location and characteristics of individual buildings and business establishments. The output of this effort is twofold: first, the analysis will obtain the factors that best explain the observed variability in curb parking demand, second, the analysis will obtain a model that can be used to predict future curb space demand.

Task 6 – Dissemination of findings and recommendations

A final report containing the result from the collection, processing, and analysis of the sensors data and establishment survey data will be drafted and published.

Expected outcomes

  • Descriptive time and spatial analysis of commercial vehicle load zone and passenger load zone utilization
  • Understand the impact of different establishments’ location and characteristics on commercial vehicle load zone and passenger load zone utilization
  • Discussion of policy implications for commercial vehicle load zones and passenger load zones allocation and time restrictions