Student Thesis and Dissertations

Ridehail and Commercial Vehicles Access in Urban Areas: Implications for Public Infrastructure Management

URL: https://www.proquest.com/docview/2652706673

Publication Date: 2022

Summary:

As urbanized populations and concentrations of activities increase, there is growing pressure in dense and constrained urban areas to unlock the potential of every public infrastructure element to address the increasing demand for public space. Specifically, there is a growing demand for space for parking operations related to the access to land use by people and goods. On one side, ridehailing services, such as those provided by Uber and Lyft, are on the rise and with them the associated passenger pick-up/drop-off (PUDOs) operations. On the other side, freight and servicing trips require a supply of adequate infrastructure to support vehicle access and load/unload activities and final delivery/service to customers. This dissertation aims to provide insights based on real-world datasets and tests to support the management of two key public infrastructure that provides access to land uses: alleys and curb lanes. To achieve this goal, first, this dissertation will investigate what roles alleys play in cities and inspect alleys’ physical characteristics and vehicle parking operations in these spaces. Secondly, this research will examine factors of PUDO dwell time and evaluate the impact of adding curb lane PUDO zones and geofencing ridehailing vehicles to these zones using a hazard-based duration modeling approach. Finally, this dissertation will analyze the impact of different ridehailing curb management strategies on curb lane utilization based on simulation.

Authors: José Luis Machado León

Recommended Citation:
León, J., Luis Machado. (2022). Ridehail and Commercial Vehicles Access in Urban Areas: Implications for Public Infrastructure Management (Order No. 10827973). University of Washington Doctoral Dissertation.

Report

The Final 50 Feet of the Urban Goods Delivery System: Completing Seattle’s Greater Downtown Inventory of Private Loading & Unloading Infrastructure (Phase 2)

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Publication Date: 2020

Summary:

This report describes the Urban Freight Lab (UFL) work to map the locations of all private loading docks, loading bays, and loading areas for commercial vehicles in Seattle’s First Hill and Capitol Hill neighborhoods and document their key design and capacity features, as part of our Final 50 Feet Research Program.

Taken together with the UFL’s earlier private freight infrastructure inventory in Downtown Seattle, Uptown, and South Lake Union, this report finalizes the creation of a comprehensive Greater Downtown inventory of private loading/unloading infrastructure. The Seattle Department of Transportation (SDOT) commissioned this work as part of its broader effort with UFL to GIS map the entire Greater Downtown commercial load/unload network, which includes alleys, curbs and private infrastructure.

The research team could find no published information on any major U.S. or European city that maintains a database with the location and features of private loading/unloading infrastructure (meaning, out of the public right of way): Seattle is the first city to do so.

By supporting and investing in this work, SDOT demonstrates that it is taking a high-level conceptual view of the entire load/unload network. The city will now have a solid baseline of information to move forward on myriad policy decisions. This commitment to creating a private load/unload infrastructure inventory is significant because infrastructure is often identified as an essential element in making urban freight delivery more efficient. But because these facilities are privately owned and managed, policymakers and stakeholders lack information about them—information critical to urban planning. By and large, this private infrastructure has been a missing piece of the urban freight management puzzle. The work represented in this section fills a critical knowledge gap that can help advance efforts to make urban freight delivery more efficient in increasingly dense, constrained cities, like Seattle.

Without having accurate, up-to-date information on the full load/unload network infrastructure—including the private infrastructure addressed here—cities face challenges in devising effective strategies to minimize issues that hamper urban freight delivery efficiency, such as illegal parking and congestion. Research has shown that these issues are directly related to infrastructure (specifically, a lack thereof). (4) A consultant report for the New York Department of Transportation found that the limited data on private parking facilities for freight precluded development of solutions that reduce double parking, congestion and other pertinent last-mile freight challenges. (5) The report also found that the city’s off-street loading zone policy remained virtually unchanged for 65 years (despite major changes like the advent and boom of e-commerce.)

Local authorities often rely heavily on outside consultants to address urban freight transport issues because these authorities generally lack in-house capacity on urban freight. (6) Cities can use the replicable data-collection method developed here to build (and maintain) their own database of private loading/unloading infrastructure, thereby bolstering their in-house knowledge and planning capacity. Appendix C includes a Step-by-Step Toolkit for a Private Load/Unload Space Inventory that cities, researchers, and other parties can freely use.

The method in that toolkit builds—and improves—on the prior data-collection method UFL used to inventory private infrastructure in the dense urban neighborhoods of Downtown Seattle, Uptown and South Lake Union in early 2017 (Phase 1). The innovative, low-cost method ensures standardized, ground-truthed, high-quality data and is practical to carry out as it does not require prior permission and lengthy approval times to complete.

This inventory report’s two key findings are:

Data collectors in this study identified, examined, and collected key data on 92 private loading docks, bays and areas across 421 city blocks in the neighborhoods of Capitol Hill, First Hill, and a small segment of the International District east of I-5. By contrast, the early 2017 inventory in Downtown Seattle, Uptown, and South Lake Union identified 246 private docks, bays and areas over 523 blocks—proportionally more than twice the density of private infrastructure of Capitol Hill and First Hill. This finding is not surprising. While all the inventoried neighborhoods are in the broad Greater Downtown, they are fundamentally different neighborhoods with different built environments, land use, and density. Variable demand for private infrastructure—and the resulting supply—stems from those differences.
A trust relationship with the private sector is essential to reduce uncertainty in this type of work. UFL members added immense value by ground-truthing this work and playing an active role in improving inventory results. When data collectors in the field found potential freight loading bays with closed doors (preventing them from assessing whether the locations were, in fact, used for freight deliveries), UPS had their local drivers review the closed-door locations as part of their work in the Urban Freight Lab. The UPS review allowed the researchers to rule out 186 of the closed-door locations across this and the earlier 2017 data collection, reducing uncertainty in the total inventory from 33% to less than 1%.

This report is part of a broader suite of UFL research to date that equips Seattle with an evidence-based foundation to actively and effectively manage Greater Downtown load/unload space as a coordinated network. The UFL has mapped the location and features of the legal landing spots for trucks across the Greater Downtown, enabling the city to model myriad urban freight scenarios on a block-by-block level. To the research team’s knowledge, no other city in the U.S. or the E.U. has this data trove. The findings in this report, together with all the UFL research conducted and GIS maps and databases produced to date, give Seattle a technical baseline to actively manage the Greater Downtown’s load/unload network to improve the goods delivery system and mitigate gridlock.

The project partners will integrate sensor technologies, develop data platforms to process large data streams, and publish a prototype app to let delivery firms 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. This is the nation’s first systematic research pilot to test proof of concept of a functioning system that offers commercial vehicle drivers and dispatchers real-time occupancy data on load/unload spaces–and test what impact that data has on commercial driver behavior. This pilot can help inform other cities interested in taking steps to actively manage their load/unload network.

Actively managing the load/unload network is more imperative as the city grows denser, the e-commerce boom continues, and drivers of all vehicle types—freight, service, passenger, ride-sharing and taxis—jockey for finite (and increasingly valuable) load/unload space. Already, Seattle ranks as the sixth most-congested city in the country.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). The Final 50 Feet of the Urban Goods Delivery System: Phase 2, Completing Seattle’s Greater Downtown Inventory of Private Loading/Unloading Infrastructure.

Report

The Final 50 Feet of the Urban Goods Delivery System: Tracking Curb Use in Seattle

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Publication Date: 2019

Summary:

Vehicles of all kinds compete for parking space along the curb in Seattle’s Greater Downtown area. The Seattle Department of Transportation (SDOT) manages use of the curb through several types of curb designations that regulate who can park in a space and for how long. To gain an evidence-based understanding of the current use and operational capacity of the curb for commercial vehicles (CVs), SDOT commissioned the Urban Freight Lab (UFL) at the University of Washington Supply Chain Transportation & Logistics Center to study and document curb parking in five selected Greater Downtown areas.

This study documents vehicle parking behavior in a three-by-three city block grid around each of five prototype Greater Downtown buildings: a hotel, a high-rise office building, an historical building, a retail center, and a residential tower. These buildings were part of the UFL’s earlier SDOT-sponsored research tracking how goods move vertically within a building in the final 50 feet.

The areas around these five prototype buildings were intentionally chosen for this curb study to deepen the city’s understanding of the Greater Downtown area.

Significantly, this study captures the parking behavior of commercial vehicles everywhere along the curb as well as the parking activities of all vehicles (including passenger vehicles) in commercial vehicle loading zones (CVLZs). The research team documented: (1) which types of vehicles parked in CVLZs and for how long, and; (2) how long commercial vehicles (CVs) parked in CVLZs, in metered parking, and in passenger load zones (PLZ) and other unauthorized spaces.

Four key findings, shown below, emerged from the research team’s work:

Commercial and passenger vehicle drivers use CVLZs and PLZs fluidly: commercial vehicles are parking in PLZs, and passenger vehicles are parking in CVLZs. Passenger vehicles made up more than half of all vehicles observed parking in CVLZs (52%). More than one-quarter of commercial vehicle drivers parked in PLZs (26 %.) This fact supports more integrated planning for all curb space, versus developing standalone strategies for passenger vehicle and for commercial vehicle parking.
Most commercial vehicle (CV) demand is for short-term parking: 15 or 30 minutes. Across the five locations, more than half (54%) of all CVs parked for 15 minutes or less in all types of curb spaces. Nearly three-quarters of all CVs (72%) parked for 30 minutes or less. When considering just the delivery CVs, an even higher percentage, 60%, parked for 15 minutes or less. Eighty-one percent of the delivery CVs parked for 30 minutes or less.
Thirty-six percent of the total CVs parked along the curb were service CVs, showing the importance of factoring their behavior and future demand into urban parking schemes. In contrast to delivery CVs that predominately parked for 30 minutes or less, service CVs’ parking behavior was bifurcated. While 56% of them parked for 30 minutes or less, 44% parked for more than 30 minutes. And more than one quarter (27%) of the service CVs parked for an hour or more. Because service vehicles make up such a big share of total CVs at the curb, this may have an outsize impact on parking space turn rates at the curb.
Forty-one percent of commercial vehicles parked in unauthorized locations. But a much higher percentage parked in unauthorized areas near the two retail centers (55% – 65%) when compared to the predominately office and residential areas (27% – 30%). The research team found that curb parking behavior is associated with granular, building-level urban land use. This occurred even as other factors such as the total number, length and ratio of CVLZs versus PLZs varied widely across the five study areas.

The occupancy study documents that each building and the built environment surrounding it has unique features that impact parking operations. As cities seek to more actively manage curb space, the study’s findings illuminate the need to plan a flexible network with capacity for distinct types (time and space requirements) of CV parking demand.

This study also drives home that the curb does not function in isolation, but instead forms one element of the Greater Downtown’s broader, interconnected load/unload network, which includes alleys, the curb, and private loading bays and docks. (1,2,3) SDOT commissioned this work as part of its broader effort with the UFL to map—and better understand—the entire Greater Downtown area’s commercial vehicle load/unload space network. Cities and other parties interested in the details of how to conduct a commercial vehicle occupancy study can see a step-by-step guide in Appendix C.

In this study, researchers deployed six data collectors to observe each curb study area for three days over roughly six weeks in October and December 2017. To make the data produced in this project as useful as possible, the research team designed a detailed vehicle typology to track specific vehicle categories consistently and accurately. The typology covers 10 separate vehicle categories, from various types of trucks and vans to passenger vehicles to cargo bikes. Passenger vehicles in this study were not treated as commercial vehicles, due to challenges in systematically identifying whether passenger vehicles were making deliveries or otherwise carrying a commercial permit.

The five prototype Seattle buildings studied are Seattle Municipal Tower (also the site of a common carrier parcel locker pilot), Dexter Horton, Westlake Center, and Insignia Towers. (4) The study shows how different building and land uses interact with the broader load/unload network. By collecting curb occupancy data in the same locations as their earlier work, the research team added a new layer of information to help the city evaluate—and manage—the Greater Downtown area load/unload network more comprehensively.

This report is part of a broader suite of UFL research to date that equips Seattle with an evidence-based foundation to actively and effectively manage Greater Downtown load/unload space as a coordinated network. The UFL has mapped the location and features of the legal landing spots for trucks across the Greater Downtown, enabling the city to model myriad urban freight scenarios on a block-by-block level. To the research team’s knowledge, no other city in the U.S. or the E.U. has this data trove. The findings in this report, together with all the UFL research conducted and GIS maps and databases produced to date, give Seattle a technical baseline to actively manage the Greater Downtown’s load/unload spaces as a coordinated network to improve the goods delivery system and mitigate gridlock.

The UFL will pilot such active management on select Greater Downtown streets in Seattle and Bellevue, Washington, to help goods delivery drivers find a place to park without circling the block in crowded cities for hours, wasting time and fuel and adding to congestion. The U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Vehicles Technologies Office is funding the project. (5) The project partners will integrate sensor technologies, develop data platforms to process large data streams, and publish a prototype app to let delivery firms 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. This is the nation’s first systematic research pilot to test proof of concept of a functioning system that offers commercial vehicle drivers and dispatchers real-time occupancy data on load/unload spaces–and test what impact that data has on commercial driver behavior. This pilot can help inform other cities interested in taking steps to actively manage their load/unload network.

The UFL is a living laboratory made up of retailers, truck freight carriers and parcel companies, technology companies supporting transportation and logistics, multifamily residential and retail/commercial building developers and operators, and SDOT. Current members are Boeing HorizonX, Building Owners and Managers Association (BOMA) – Seattle King County, curbFlow, Expeditors International of Washington, Ford Motor Company, General Motors, Kroger, Michelin, Nordstrom, PepsiCo, Terreno, USPack, UPS, and the United States Postal Service (USPS).

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2019). The Final 50 Feet of the Urban Goods Delivery System: Tracking Curb Use in Seattle.

Technical Report

Development and Analysis of a GIS-Based Statewide Freight Data Flow Network

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URL: https://rosap.ntl.bts.gov/view/dot/39538

Publication: Washington State Department of Transportation

Publication Date: 2009

Summary:

In the face of many risks of disruptions to our transportation system, this research improves WSDOT’s ability to manage the freight transportation system so that it minimizes the economic consequences of transportation disruptions.

Faced with a high probability that major disruptions to the transportation system will
harm the state’s economy, the Washington State Department of Transportation
(WSDOT), in partnership with Transportation Northwest (TransNow) commissioned
researchers at the University of Washington and Washington State University to
undertake freight resiliency research to:

Understand how disruptions of the state’s freight corridors change the way
trucking companies and various freight-dependent industries route goods,
Plan to protect freight-dependent sectors that are at high risk from these disruptive
events, and
Prioritize future transportation investments based on the risk of economic loss to
the state

To accurately predict how companies will route shipments during a disruption,
this research developed the first statewide multimodal freight model for Washington
State. The model is a GIS-based portrayal of the state’s freight highway, arterial, rail,
waterway and intermodal network and can help the state prioritize strategies that protect industries most vulnerable to disruptions.

The report features two case studies showing the model’s capabilities: the potato growing and processing industry was chosen as a representative agricultural sector, and diesel fuel distribution for its importance to all industry sectors. The case studies are found in sections 5.2 and 5.3 in the report and show how the statewide freight model can:

Predict how shipments will be re-routed during disruptions, and
Analyze the level of resiliency in various industry sectors in Washington State

The two case studies document the fragility of the state’s potato growing and processing
sectors and its dependence on the I-90 corridor, while showing how the state’s diesel
delivery system is highly resilient and isn’t linked to I-90.

As origin-destination data for other freight-dependent sectors is added to the model,
WSDOT will be able to evaluate the impact of freight system disruptions on each of
them. This will improve WSDOT’s ability to develop optimal strategies for highway
closures, and prioritize improvements to the system based on the relative impact of the
disruption.

This research addressed several technical areas that would need to be resolved by any
organization building a state freight model. First, the researchers had to decide on the
level of spatial and temporal detail to include in the statewide GIS freight model. This
decision has significant consequences for data resolution requirements and results.
Including every road in Washington would have created a cumbersome model with a
large number of links that weren’t used. However, in order to analyze routing during a
disruption all possible connections must exist between origin and destination points in the model. While the team initially included only the core freight network in the model,
ultimately all road links were added to create complete network connectivity.

Second, as state- and corridor-level commodity flow data is practically non-existent, data
collection for the two case studies was resource intensive. Supply chain data is held by
various stakeholders and typically not listed on public websites, and it isn’t organized by
those stakeholders for use in a freight model. In most cases it’s difficult to assure data
quality. The team learned that the most difficult data to obtain is data on spatially or
temporally variable attributes, such as truck location and volume. So they developed a
method to estimate the importance of transportation links without commodity flow data.

Third, the freight model identified the shortest route, based on travel time, between any
origin and destination (O/D) pair in the state, and the shortest travel-time re-route for
each O/D pair after a disruption. The routing logic in the model is based on accepted
algorithms used by Google Maps and MapQuest. Phase III of the state’s freight
resiliency research was funded by WSDOT and will result in improved truck freight
routing logic for the model in 2011.

The two case studies showed how the state’s supply chains use infrastructure differently,
and that some supply chains have built flexibility into their operations and are resilient
while others are not, which leads to very different economic consequences. The results
of these case studies significantly contributed to WSDOT’s understanding of goods
movement and vulnerability to disruptions.

In the future, Washington State will need corridor-level commodity flow data to
implement the research findings and complete the state freight model. In 2009, the
National Cooperative Freight Research Program (NCFRP) funded development of new
methodology to collect and analyze sub-national commodity flow information. This
NCFRP project, funded at $500,000, will be completed in 2010 and provide a mechanism for states to accurately account for corridor-level commodity flows. If funds are available to implement the new methodology in Washington State, the state’s freight
model will use the information to map these existing origin destination commodity flows
onto the freight network, evaluate the number of re-routed commercial vehicles, and their increased reroute distance from any disruption. This will allow WSDOT to develop
prioritized plans for supply chain disruptions, and recommend improvements to the
system based on the economic impact of the disruption.

This report summarizes 1) the results from a thorough review of resilience literature and resilience practices within enterprises and organizations, 2) the development of a GIS-based statewide freight transportation network model, 3) the collection of detailed data regarding two important industries in Washington state, the distribution of potatoes and diesel fuel, and 4) analysis of the response of these industries to specific disruptions to the state transportation network.

The report also includes recommendations for improvements and additions to the GIS model that will further the state’s goals of understanding the relationship between infrastructure availability and economic activity, as well as recommendations for improvements to the statewide freight transportation model so that it can capture additional system complexity.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Eric Jessup, Derik Andreoli, Kelly Pitera, Sunny Rose, Chilan Ta

Recommended Citation:
Goodchild, Anne V., Eric L. Jessup, Edward D. McCormack, Derik Andreoli, S Rose, Chilan Ta and Kelly Pitera. “Development and Analysis of a GIS-Based Statewide Freight Data Flow Network.” (2009).