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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URL: https://dx.doi.org/10.2139/ssrn.4183322

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.

Authors: Dr. Anne GoodchildJosé Luis Machado LeónGabriela Girón-Valderrama

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

Paper

An Empirical Taxonomy of Common Curb Zoning Configurations in Seattle

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URL: https://doi.org/10.32866/001c.32446

Publication: Findings

Publication Date: 2022

Summary:

We utilize an unsupervised learning algorithm called-modes clustering (Huang 1998), which is similar to the better-known-means method (Hartigan and Wong 1979), but with a dissimilarity measure designed for categorical variables (Cao et al. 2012), originally developed for analyzing sequential categorical data such as gene sequences (Goodall 1966), but also amenable to curb zoning types. For a specified, the-modes algorithm finds the top vectors that minimize a distance to all sample vectors in the training dataset. The resulting top modes are representative of distinct clusters of sample vectors, with cluster membership determined by the closest mode. The parameter is chosen through cross-validation by holding out portions of the available training data and finding the smallest that largely minimizes the within-cluster variation in this hold-out set (also called the “elbow method”). We utilize basic matching dissimilarity, as implemented in (Vos 2015). For two vectors and of length, where each element attains categorical values, matching dissimilarity is defined as, where denotes the indicator vector, with value 1 where the bracketed condition is true and 0 otherwise. We’ve chosen this measure of dissimilarity between two sets of categorical variables for a number of reasons: 1) its simplicity, 2) successful use in categorical data clustering (Goodall 1966), and 3) its sensitivity to the ordering of values when vectors and are ordered, specific to how we have chosen to represent curb zoning data.

Authors: Thomas MaxnerDr. Andisheh Ranjbari, Chase Dowling

Recommended Citation:
Dowling, Chase P., Thomas Maxner, and Andisheh Ranjbari. 2022. “An Empirical Taxonomy of Common Curb Zoning Configurations in Seattle.” Findings, February. https://doi.org/10.32866/001c.32446

Paper

Commercial Vehicle Parking in Downtown Seattle: Insights on the Battle for the Curb

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URL: https://doi.org/10.1177%2F0361198119849062

Publication: Transportation Research Record: Journal of the Transportation Research Board

Publication Date: 2019

Summary:

Rapid urban growth puts pressure on local governments to rethink how they manage street curb parking. Competition for space among road users and lack of adequate infrastructure force delivery drivers either to search for vacant spaces or to park in unsuitable areas, which negatively impacts road capacity and causes inconvenience to other users of the road.

The purpose of this paper is to advance research by providing data-based insight into what is actually happening at the curb. To achieve this objective, the research team developed and implemented a data collection method to quantify the usage of curb space in the densest urban area of Seattle, Center City.

This study captures the parking behavior of commercial vehicles everywhere along the block face as well as the parking activities of all vehicles (including passenger vehicles) in commercial vehicle loading zones. Based on the empirical findings, important characteristics of Seattle’s urban freight parking operations are described, including a detailed classification of vehicle types, dwell time distribution, and choice of curb use for parking (e.g., authorized and unauthorized spaces). The relationship between land use and commercial vehicle parking operations at the curb is discussed. Seattle’s parking management initiatives will benefit from the insights into current behavior gained from this research.

Rapid urban growth, increasing demand, and higher customer expectations have amplified the challenges of urban freight movement. Finding an adequate space to park can be a major challenge in urban areas. For commercial vehicles used for freight transportation and provision of services, the lack of parking spaces and parking policies that recognize those vehicles’ unique needs can have negative impacts that affect all users of the road, particularly the drivers of these commercial vehicles (1–4).

The curb is an important part of the public right-of-way. It provides a space for vehicles to park on-street; for delivery vehicles (i.e., cargo bikes, cargo vans, and trucks), in particular, it also provides a dedicated space for the loading and unloading of goods close to destinations. Hence it is a key asset for urban freight transportation planning which local governments can administer to support delivery and collection of goods.

According to Marcucci et al. (5), the development of sustainable management policies for urban logistics should be based on site-specific data given the heterogeneity and complexity of urban freight systems. Current loading/unloading parking policies include time restrictions, duration, pricing, space management, and enforcement (6, 7). However, as Marcucci et al. pointed out after an extensive review of the literature on freight parking policy, the quantification of commercial vehicle operations on the curb to inform policy decision making is nonexistent (5). Therefore, local governments often lack data about the current usage of the curb and parking infrastructure, which is necessary to evaluate and establish these policies and therefore make well-informed decisions regarding freight planning, especially in dense, constrained urban areas.

Given the importance of the curb as an essential piece of the load/unload infrastructure, this paper investigates what is actually happening at the curb, developing an evidence-based understanding of the current use of this infrastructure. The research team developed and applied a systematic data collection method resulting in empirical findings about the usage of public parking for loading and unloading operations in the Seattle downtown area.

This research documents and analyzes the parking patterns of commercial vehicles (i.e., delivery, service, waste management, and construction vehicles) in the area around five prototype buildings located in the Center City area. The results of this research will help to develop and inform parking management initiatives.

The paper includes four sections in addition to this introduction. The second section discusses previous freight parking studies and the existing freight parking policies in cities, and explores which of these approaches are being used in Seattle. The third section proposes a data collection method to document freight-related parking operations at the curb though direct observations. The fourth section provides empirical findings from data collection in Seattle. The fifth and last section includes a discussion of the findings and concluding remarks.

Authors: Gabriela Girón-ValderramaJosé Luis Machado LeónDr. Anne Goodchild

Recommended Citation:
Girón-Valderrama, Gabriela del Carmen, José Luis Machado-León, and Anne Goodchild. "Commercial Vehicle Parking in Downtown Seattle: Insights on the Battle for the Curb." Transportation Research Record (2019): 0361198119849062.

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.

Urban Delivery Companies’ Needs and Preferences for Green Loading Zones’ Implementation: A Case Study of NYC

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URL: https://doi.org/10.1061/9780784484340

Publication: International Conference on Transportation and Development 2022

Publication Date: 2022

Summary:

Green loading zones (GLZs) are curb spaces dedicated to the use of electric or alternative fuel (“green”) delivery vehicles. Some US cities have begun piloting GLZs to incentivize companies to purchase and operate more green vehicles. However, there are several questions to be answered prior to a GLZ implementation, including siting, potential users, and their willing to pay. We reviewed best practices for GLZs around the world and surveyed goods delivery companies operating in New York City to collect such information for a future GLZ pilot. The findings suggest the best candidate locations are areas where companies are currently subject to the most parking fines and double parking. Companies expressed willingness to pay for GLZs, as long as deploying green vehicles in the city can offset other cost exposures. Respondents also selected several single-space GLZs spread throughout a neighborhood as the preferred layout.

Authors: Thomas MaxnerNota GoulianouDr. Andisheh RanjbariDr. Anne Goodchild

Student Thesis and Dissertations

Observing Goods Delivery Activities and Identifying Opportunities to Improve the Design of Commercial Vehicle Load Zones in Seattle

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URL: http://hdl.handle.net/1773/42557

Publication Date: 2018

Summary:

The growth of freight activity is one of the results of urban population growth. The growth of freight means that more commercial vehicles must share finite infrastructure like alleys, loading docks, and yellow curb space. In this research project, curb space is studied in order to better understand the needs of commercial vehicles at the curb. Cities in the United States like Seattle have recognized that there are opportunities to better manage curb space, and have implemented programs such as the Flex Zone Program 2016 in order to do so. In this research paper, I have focused on just one aspect of the curb, which is the yellow curb space reserved for Commercial Vehicle Load Zones (CVLZ). The purpose of this thesis is to observe the needs and activities of courier drivers during deliveries/pickups in Seattle, and incorporate observations into a new design of freight curb space that may better respond to their needs. The new design suggests a system in which curb space is designed for different vehicle dimensions and activities. This is done by including paint, texture/pattern, and signage on the pavement and sidewalk that comfortably accommodate the vehicle and activities around the vehicle. By providing a better designed freight curb space that accounts for the needs and activities observed, the hope is that courier drivers will be less likely to partake in high-risk behavior such as double parking, and spilling over into adjacent transit lanes/pedestrian areas/bikes lanes, by providing better infrastructure for them.

Authors: Manali Sheth

Recommended Citation:
Sheth, Manali (2018). Observing Goods Delivery Activities and Identifying Opportunities to Improve the Design of Commercial Vehicle Load Zones in Seattle. University of Washington Master's Degree Thesis.

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.