Article

Giving Curb Visibility to Delivery Drivers

URL: https://issuu.com/apatransport/docs/2022_sotp/s/16021615

Publication: American Planning Association | 2022 State of Transportation Planning

Pages: 134-143

Publication Date: 2022

Summary:

At the time we are writing this article, hundreds of thousands of delivery vehicles are getting ready to hit the road and travel across U.S. cities to meet the highest peak of demand for ecommerce deliveries during Thanksgiving, Black Friday, and the Christmas holiday season. This mammoth fleet will not only add vehicle miles traveled through urban centers but also increase parking congestion, battling with other vehicles for available curb space.

While the integration of road traffic data with modern navigation systems has seen huge developments in the past decade, drastically changing the way we, and delivery vehicles, navigate through cities, not as much can be said when it comes to parking. The task of finding and securing parking is still left to drivers, and largely unsupported by real-time information or app-based solutions.

Delivery vehicle drivers are affected by curb parking congestion even more than any other driver because delivery drivers have to re-park their vehicles not once or twice, but 10, 20, or even more times during a delivery route.

Our work, discussed in this article, focuses on improving delivery drivers’ lives when it comes to finding available curb space, improving the delivery system, and reducing some of the externalities generated in the process. We first describe what types of parking behaviors delivery drivers adopt when facing a lack of available curb parking, then we will quantify the cost of lack of available parking, estimating how much time delivery drivers spend circling the block and searching for parking. We then discuss how we can improve on that by creating the first curb availability information system – OpenPark.

Authors: Dr. Giacomo Dalla ChiaraDr. Anne Goodchild

Recommended Citation:
Dalla Chiara, Giacomo and Anne Goodchild. Giving Curb Visibility to Delivery Drivers. Intersections + Identities: State of Transportation Planning 2022, 134-143.

Technical Report

Year One Progress Report: 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

Download PDF (5.08 MB)

Publication: U.S. Department of Energy

Publication Date: 2019

Summary:

The objectives of this project are to develop and implement a technology solution to support research, development, and demonstration of data processing techniques, models, simulations, a smart phone application, and a visual-confirmation system to:

Reduce delivery vehicle parking seeking behavior by approximately 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 areas in Seattle and Bellevue, Washington, by approximately 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems, and
Improve the transportation network (which includes roads, intersections, warehouses, fulfillment centers, etc.) and commercial firms’ efficiency by increasing curb occupancy rates to roughly 80%, and alley space occupancy rates from 46% to 60% during peak hours, and increasing private loading bay occupancy rates in the afternoon peak times, in the pilot test area.

The project team has designed a 3-year plan, as follows, to achieve the objectives of this project.

In Year 1, the team developed integrated technologies and finalized the pilot test parameters. This involved finalizing the plan for placing sensory devices and common parcel locker systems on public and private property; issuing the request for proposals; selecting vendors; and gaining approvals necessary to execute the plan. The team also developed techniques to preprocess the data streams from the sensor devices, and began to design the prototype smart phone parking app to display real-time load/unload space availability, as well as the truck load/unload space behavior model.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). Year One Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.

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.

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.

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.

Report

Curbing Conflicts: Curb Allocation Change Project Report

Download PDF (4.05 MB)

Publication Date: 2019

Summary:

Like many congested cities, Seattle is grappling with how best to manage the increasing use of ride-hailing services by Transportation Network Companies (TNCs) like Uber and Lyft. According to a 2018 Seattle Times analysis, TNC ridership in the Seattle region has grown to more than five times the level it was in the beginning of 2015, providing, on average, more than 91,000 rides a day in 2018. And the newspaper reports Uber and Lyft trips are heavily concentrated in the city’s densest neighborhoods, where nearly 40,000 rides a day start in ZIP codes covering downtown, Belltown, Capitol Hill and South Lake Union.

This University of Washington (UW) study focuses on a strategy to manage TNC driver stops when picking up and dropping off passengers to improve traffic flow in the South Lake Union (SLU) area. SLU is the site of the main campus for Amazon, the online retail company. The site is known to generate a large number of TNC trips, and Amazon reports high rates of ride-hailing use for employee commutes. This study also found that vehicle picking-up/dropping-off passengers make up a significant share of total vehicle activity in SLU. The center city neighborhood is characterized by multiple construction sites, slow speed limits (25 mph), and heavy vehicle and pedestrian traffic.

Broad concerns about congestion, safety, and effective curb use led to this study, conducted by researchers at the UW’s Urban Freight Lab and Sustainable Transportation Lab. Amazon specifically was concerned about scarcity of curb space where TNC drivers could legally and readily stop to pick up and drop off passengers. Without dedicated load/unload curb space, TNC vehicles stop and wait at paid parking spots, other unauthorized curb spots, or in the travel lane itself, potentially blocking or slowing traffic. To try to mitigate the impacts of passenger pick-up/drop-off activity on traffic, the city proposed a strategy of increasing passenger loading zone (PLZ) spaces while Uber and Lyft implemented a geofence, which directs their drivers and passengers to designated pick-up and drop-off locations on a block. (Normally, drivers pick up or drop off passengers at any address a rider requests via the ride-hailing app.)

By providing ample designated pick-up and drop-off spots along the curb, the thinking goes, TNC drivers would reduce the frequency with which they stop in the travel lane to pick up or drop off passengers and the time they stay stopped there. By these measures, this study’s findings show the approach was successful. But it is important to note that the strategy is not a silver bullet for solving traffic congestion—nor is it designed as such. It is also important to note that any initiative to manage use of curbs and roads (by TNCs or others) is part of a city’s broader transportation policy framework and goals.

For this study, researchers analyzed an array of data on street and curb activity along three block-faces on Boren Ave N in December 2018 and January 2019. At a minimum, data were collected during the morning and afternoon peak travel times (with some collected 24 hours a day). The research team collected data using video and sensor technology as well as in-person observation. Researchers also surveyed TNC passengers for demographic, trip-related and satisfaction data. The five Amazon buildings in the area studied house roughly 8,650 employees. Researchers collected data in three stages. Phase 1, the study baseline, was before PLZs were added and geofencing started. Phase 2 was after the new PLZs were added, expanding total PLZ curb length from 20 feet (easily filled by one to two vehicles) to 274 feet. Phase 3 was after geofencing was added to the expanded PLZs. The added PLZ spaces were open to any passenger vehicle—not just TNC vehicles—weekdays from 7am to 10am and 2pm to 7pm. (Permitted food trucks were authorized from 10am to 2pm.)

Note that while other cities can learn from this analysis, the findings apply to streets with comparable traffic speed, mix of roadway users, and street design.

The study’s main findings include:

A significant percentage of vehicles performing a pick-up/drop-off stop in the travel lane. Those in-lane stops appear connected to the lack of available designated curb space: Adding PLZs and geofencing increased driver compliance in stopping at the curb versus stopping in the travel lane to load and unload passengers. But it was not lack of curb space alone that influenced driver activity: Between 7 percent and 10 percent of drivers still stopped in the travel lane even when PLZs were empty. After adding PLZs and geofencing, in-lane stops fell from 20 percent to 14 percent for pick-ups and from 16 percent to 15 percent for drop-offs.
Adding PLZs and geofencing reduced the average amount of time drivers stopped to load and unload passengers. For example, 90 percent of drop-offs took less than 1 minute 12 seconds, 42 seconds faster than the average with the added PLZs alone.
While curb occupancy increased after adding PLZs and geofencing, occupancy results show the current allocation of PLZ spaces is more than what is needed to meet observed demand: Average PLZ occupancy remained under 20 percent after PLZ expansion, even during peak commute hours.
Vehicles picking-up/dropping-off passengers account for a significant share of total traffic volume in the study area: during peak hours the observed average percentage of vehicles performing a pick-up/drop-off with respect to the total traffic volume was 29 percent (in Phase 1), 32 percent (in Phase 2) and 39 percent (in Phase 3).
High volumes of pedestrians (400-500 per hour on average) cross the street at points where there was no crosswalk. Passengers picked-up/dropped-off constituted a fraction (five to seven percent) of those pedestrians, but high rates of passengers (30 to 40 percent) cross the street at non-crosswalk locations.
Adding PLZs and geofencing did not have a significant impact on traffic safety. Researchers found no significant change in the number of observed conflicts from baseline to the addition of PLZs and geofencing. Conflicts are situations where a vehicle, bike, or pedestrian is interrupted, forced to alter their path, or engaged in a near-miss situation. Conflicts include vehicles passing in the oncoming traffic lane. • Adding PLZs and geofencing also did not produce a significant impact on roadway travel speed.
Of the 116 TNC passengers surveyed in the study area:
- Roughly 40 percent to 50 percent said their trip was work related. More than half said they used ride-hailing service at least once a week and 70 percent or more used TNC alone (versus in combination with other transportation options) to get from their origin to their destination.
- Most responded positively to the added PLZs and geofence: 79 percent rated their pick-up satisfactory and 100 percent rated their drop-off satisfactory as compared to 72 percent and 89 percent in the baseline.
- Nearly half said they would have taken transit and one-third would have walked if ride-hailing was not available.
- 40 percent requested a shared TNC vehicle in Phase 1 and 47 percent in Phase 3.

The study suggests that while vehicles picking-up/dropping-off passengers account for a significant share of traffic volume in SLU, they are not the primary cause of congestion. Myriad factors impact neighborhood congestion, including high vehicle volume overall and bottlenecks moving out of the neighborhood onto regional arterials. As researchers observed in the afternoon peak, these bottlenecks cause spillbacks onto local streets. Amazon garages exit vehicles onto streets that then feed into these clogged arterials.

Regarding traffic safety in SLU, this study was not designed to assess whether TNC driver behavior on average is safer or less safe than that of other vehicles. It is important to understand the safety and speed findings in the context of the SLU traffic environment. Drivers tend to drive at relatively slow speeds, navigating around high pedestrian and jaywalking volumes, and seem relatively comfortable stopping in the middle of the street for short periods of time. Due to the nature of area traffic, this seems to have relatively little impact on other drivers. Drivers appear to anticipate both this behavior and the high volumes of vehicles moving onto/off the curb and into/out of driveways and alleys.

Whether the strategy this study analyzed is recommended depends on a city’s transportation goals and approach. The researchers found the increased PLZ allocation and geofencing strategy worked in that it improved driver compliance, reduced dwell times, and boosted TNC user satisfaction. However, this may encourage commuters to use TNC. The passenger survey clearly shows that TNC service is attracting passengers who would have otherwise walked or used transit. While in the short term the increased PLZs and geofencing had a positive effect on traffic, if this induces TNC demand, there could be larger, more negative long-term consequences. If the end goal is to reduce traffic congestion, measures to reduce—rather than encourage—TNC and passenger car use as the predominant mode of commuting will yield the most substantial benefits.

In the news:

Geekwire: As Uber and Lyft pick-ups and drop-offs clog traffic, new study calls load zones a move in right direction

The Seattle Times: Seattle Uber and Lyft drivers often stop in the street to pick up or drop off riders. Here’s a way to reduce that.

Authors: Dr. Anne GoodchildJosé Luis Machado LeónDr. Giacomo Dalla ChiaraDr. Andisheh Ranjbari, Don MacKenzie

Recommended Citation:
Goodchild, Anne. Giacomo dalla Chiara. Jose Luis Machado. Andisheh Ranjbari. (2019) Curb Allocation Change Project.

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

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.

Seattle Center City Alley Infrastructure Inventory and Occupancy Study 2018 (Task Order 4)

The Urban Freight Lab conducted an alley inventory and truck load/unload occupancy study for the City of Seattle. Researchers collected data identifying the locations and infrastructure characteristics of alleys within Seattle’s One Center City planning area, which includes the downtown, uptown, South Lake Union, Capitol Hill, and First Hill urban centers. The resulting alley database includes GIS coordinates for both ends of each alley, geometric and traffic attributes, and photos. Researchers also observed all truck load/unload activity in selected alleys to determine minutes vacant and minutes occupied by trucks, vans, passenger vehicles, and cargo bikes. The researchers then developed alley management recommendations to promote safe, sustainable, and efficient goods delivery and pick-up.

Key Findings

The first key finding of this study is that more than 90% of Center City alleys are only one-lane wide. This surprising fact creates an upper limit on alley parking capacity, as each alley can functionally hold only one or two vehicles at a time. Because there is no room to pass by, when a truck, van, or car parks it blocks all other vehicles from using the alley. When commercial vehicle drivers see that an alley is blocked they will not enter it, as their only way out would be to back up into street traffic. Seattle Municipal code prohibits this, as well as backing up into an alley, for safety reasons.

When informed by the second key finding‚ 68% of vehicles in the alley occupancy study parked there for 15 minutes or less‚ it is clear that moving vehicles through alleys in short time increments is the only reasonable path to increase productivity. As one parked vehicle operationally blocks the entire alley, the goal of new alley policies and strategies should be to reduce the amount of time alleys are blocked to additional users.

The study surfaces four additional key findings:

87% of all vehicles in the 7 alleys studied parked for 30 minutes or less. Given the imperative to move alley traffic quickly, vehicles that need more parking time must be moved out of the alleys and onto the curb where they don’t block others.
15% of alleys’ pavement condition is so poor that delivery workers can’t pass through with loaded hand carts. Although trucks can drive over fairly uneven pavement without difficulty, it is not the case for delivery people walking with fully loaded handcarts. The alley pavement rating was done with a qualitative visual inspection to identify obvious problems; more detailed measurements would be needed to fully assess conditions.
73% of Center City area alleys contain entrances to passenger parking facilities. Placing garage entrances in alleys has been a city policy goal for years. But it increases the frequency of cars in alleys and adds demands on alley use. Understanding why cars are queuing for passenger garages located off alleys, and providing incentives and disincentives to reduce that, would help make alleys more productive.
Alleys are vacant about half of the time during the business day. While at first blush this suggests ample capacity, the fact that an alley can only hold one-to-two parked trucks at a time means alleys are limited operationally and therefore are not a viable alternative to replace the use of curb CVLZs on city streets.

These findings indicate that, due to the fixed alley width constraint, load/unload space inside Seattle’s existing Center City area alleys is insufficient to meet additional future demand.

Report

Final Report: 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

Download PDF (7.07 MB)

Publication Date: 2022

Summary:

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps.

All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions.

Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive.

To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to:

Reduce parking-seeking behavior in the study area by 20%
Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%
Increase curb space, alley space, and private loading bay occupancy rates in the study area

The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighborhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analyzed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

Key findings that provide needed context for piloting promising urban delivery solutions:

After developing a novel model using GPS data to measure parking-seeking behavior, researchers were able to quantify that, on average, a delivery driver spends 28% of travel time searching for parking, totaling on average one hour per day for a parcel delivery driver. This project offers the first empirical proof of delivery drivers’ cruising for parking.
While many working models to date have assumed that urban delivery drivers always choose to double-park (unauthorized parking in the travel lane), this study found that behavior is rare: Double parking happened less than 5% of the times drivers parked.
That said, drivers do not always park where they are supposed to. The research team found that CVLZ parking took place approximately 50% of the time. The remaining 50% included mostly parking in “unauthorized” curb spaces, including no-parking zones, bus zones, entrances/exits of parking garages, etc.
Researcher ride-alongs with delivery drivers revealed parking behaviors other than unauthorized parking that waste valuable time and fuel: re-routing (after failing to find a desired space, giving up and doubling back to the delivery destination later in the day) and queuing (temporarily parking in an alternate location and waiting until the desired space becomes available).
Some 13% of all parking events in CVLZ spaces were estimated as overstays; the figure was 80% of all parking events in PLZ spaces. So, the curb is not being used efficiently or as the city intended as many parking events exceed the posted time limit.
Meantime, there is unused off-street capacity that could be tapped in Seattle’s Central Business District. Estimates show private loading bays could increase area parking capacity for commercial vehicles by at least 50%. But surveys show reported use of loading bays is low and property managers have little incentive to maximize it. Property managers find curb loading zones more convenient; it seems delivery drivers do, too, as they choose to park at the curb even when loading bay space is available.

Key findings from the technology and strategies employed:

Carriers give commercial drivers routing tools that optimize delivery routes by considering travel distance and (often) traffic patterns—but not details on parking availability. Limited parking availability can lead to significant driver delays through cruising for parking or rerouting, and today’s drivers are largely left on their own to assess and manage their parking situation as they pull up to deliver.

The project team worked closely with the City of Seattle to obtain permission to install parking sensors in the roadway and communications equipment to relay sensor data to project servers. The team also developed a fully functional and open application that offers both real-time parking availability and near-time prediction of parking availability, letting drivers pick forecasts 5, 15, or 30 minutes into the future depending on when the driver expects to arrive at the delivery destination. Drivers can also enter their vehicle length to customize availability information.

After developing, modeling, and piloting the real-time and forecasting parking app, researchers conducted an experiment to determine how use of the app impacted driver behavior and transportation outcomes. They found that:

Having access to parking availability via the app resulted in a 28% decrease in the time drivers spent cruising for parking. Exceeding our initial goal of reducing parking seeking behavior by 20%. In the study experiment, all drivers had the same 20-foot delivery van and the same number of randomly sampled delivery addresses in the study area. But some drivers had access to the app; others did not.
Preliminary results based on historic routing data show that the use of such a real-time curb parking information and prediction app can reduce route time by approximately 1.5%. An analysis collected historic parking occupancy and cruising information and integrated it into a model that was then used to revise scheduling and routing. This model optimally routed vehicles to minimize total driving and cruising time. However, since the urban environment is complex and consists of many random elements, results based on historic data underly a high amount of randomness. Analysis on synthetic routes suggests including parking availability in routing systems is especially promising for routes with high delivery density and with stops where the cruising time delays vary a lot along the planned time horizon; here, route time savings can reach approximately 20.4% — conditions outlined in the report.
The central tradeoff among four approaches to parking app architecture going forward is cost and accuracy. The research team found that it is possible to train machine learning models using only data from curb occupancy sensors and reach a higher than 90% accuracy. Training of state-space models (those using inputs such as time of day, day of the week, and location to predict future parking availability) is computationally inexpensive, but these models offer limited accuracy. In contrast, deep-learning models are highly accurate but computationally expensive and difficult to use on streaming data.

Common carrier lockers create delivery density, helping delivery people complete their work faster. 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 (or floor-to-floor inside a building), it cuts the time their truck needs to be parked, increasing turnover and adding parking capacity in crowded cities. This project piloted and collected data on common carrier lockers in three study area buildings.

From piloting the common carrier parcel lockers, researchers found that:

The implementation of the parcel locker allowed delivery drivers to increase productivity: 40%-60% reduction in time spent in the building and 33% reduction in vehicle dwell time at the curb.

Authors: Dr. Anne GoodchildDr. Giacomo Dalla ChiaraFiete KruteinDr. Andisheh RanjbariDr. Ed McCormackElizabeth Guzy, Dr. Vinay Amatya (PNNL), Ms. Amelia Bleeker (PNNL), Dr. Milan Jain (PNNL)

Recommended Citation:
Urban Freight Lab (2022). Final Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.