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Seattle Microhub Delivery Pilot: Evaluating Emission Impacts and Stakeholder Engagement

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Publication: Case Studies on Transport Policy
Publication Date: 2023

Urban freight deliveries using microhubs and e-cargo cycles have been gaining attention in cities suffering from congestion and emissions. E-cargo cycle deliveries and microhubs used as transshipment points in urban cores can replace trucks to make cities more livable. This study describes and empirically evaluates an e-cargo tricycle pilot conducted with multi-sector stakeholders in Seattle to report the potential benefits and pitfalls of such practices. The pilot held stakeholder workshop sessions to collect inputs of interest and expectations from the project. Mobile devices used by drivers on e-cargo tricycle and cargo van routes collected delivery data to use for empirical assessment. Total vehicle miles traveled and tailpipe carbon emissions served as performance metrics when comparing e-cargo tricycle and cargo van deliveries. The results showed the net-benefit of the microhub and e-cargo tricycle routes depend on the upstream operations when replenishing packages.

The participatory approach to pilot design also provided insights into the factors of a successful pilot, with implications for scaling future e-cargo cycle delivery systems in North American cities. Namely, microhubs’ ability to host alternative revenue sources and value-added services is a boon for long-term financial competitiveness. However, lack of digital/physical infrastructure and work training/regulations specific to e-cargo cycle delivery operations present a barrier.

Recommended Citation:
Gunes, Seyma, Travis Fried, and Anne Goodchild. “Seattle Microhub Delivery Pilot: Evaluating Emission Impacts and Stakeholder Engagement.” Case Studies on Transport Policy. Elsevier BV, November 2023.

Evaluation of Sound Transit Train Stations and Transit-Oriented Development Areas for Common Carrier Locker Systems (Executive Summary)

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Publication Date: 2018

The rapid expansion of ecommerce has flooded American cities with delivery trucks, just as those cities are experiencing booming population growth. Retailers need a more efficient, reliable, and cost-effective way to deliver goods in increasingly crowded urban environments. For their part, cities like Seattle want to minimize traffic congestion, both sustain quality of life for residents and ensure a smooth flow of goods and services.

Common carrier parcel lockers hold tremendous potential for streamlining the urban goods delivery system and addressing these challenges. This research study explores the viability of providing public right of way for common carrier lockers at or near transit stations in Seattle, a ground-breaking step toward improving freight delivery in the city’s fast-growing urban core.

Recommended Citation:
Supply Chain Transportation & Logistics Center. (2018) Evaluation of Sound Transit Train Stations and Transit Oriented Development Areas for Common Carrier Locker Systems (Executive Summary)

How Many Amazon Packages Get Delivered Each Year?

Publication: The Conversation
Publication Date: 2022

How many Amazon packages get delivered each year? – Aya K., age 9, Illinois

It’s incredibly convenient to buy something online, right from your computer or phone. Whether it’s a high-end telescope or a resupply of toothpaste, the goods appear right at your doorstep. This kind of shopping is called “e-commerce” and it’s becoming more popular each year. In the U.S., it has grown from a mere 7% of retail purchases in 2012 to 19.6% of retail and $791.7 billion in sales in 2020.

Amazon’s growing reach
For Amazon, the biggest player in e-commerce, this means delivering lots of packages.

In 2021 Amazon shipped an estimated 7.7 billion packages globally, based on its nearly $470 billion in sales.

In 2021 Amazon shipped an estimated 7.7 billion packages globally.

If each of these packages were a 1-foot square box and they were stacked on top of one another, the pile would be six times higher than the distance from the Earth to the Moon. Laid end to end, they would wrap around the Earth 62 times.

Back in the early 2010s, most things bought from were shipped using a third-party carrier like FedEx or UPS. In 2014, however, Amazon began delivering packages itself with a service called “Fulfilled by Amazon.” That’s when those signature blue delivery vans started appearing on local streets.

Since then, Amazon’s logistics arm has grown from relying entirely on other carriers to shipping 22% of all packages in the U.S. in 2021. This is greater than FedEx’s 19% market share and within striking distance of UPS’s 24%. Amazon’s multichannel fulfillment service allows other websites to use its warehousing and shipping services. So your order from Etsy or eBay could also be packed and shipped by Amazon.

The supply chain
To handle that many packages, shipping companies need an extensive network of manufacturers, vehicles and warehouses that can coordinate together. This is called the supply chain. If you’ve ever used a tracking number to follow a package, you’ve seen it in action.

People who make decisions about where to send vehicles and how to route packages are constantly trying to keep costs down while still getting packages to customers on time. The supply chain can do this very effectively, but it also has downsides.

More delivery vehicles on the road produce more greenhouse gas emissions that contribute to climate change, along with pollutants like nitrogen oxides and particulate matter that are hazardous to breathe. Traffic congestion is also a major concern in cities as delivery drivers try to find parking on busy streets.

Urban freight solutions
Are there ways to balance the increasing number of deliveries while making freight safe, sustainable and fast? At the University of Washington’s Urban Freight Lab, we work with companies like Amazon and UPS and others in the shipping, transportation and real estate sectors to answer questions like this. Here are some solutions for what we and our colleagues call the “last mile” – the last leg of a package’s long journey to your doorstep.

  • Electrification: Transitioning from gasoline and diesel vehicles to fleets of electric or other zero-emission vehicles reduces pollution from delivery trucks. Tax credits and local policies, such as creating so-called green loading zones and zero-emission zones for clean vehicles, create incentives for companies to make the switch.
  • Common carrier lockers: Buildings can install lockers at central locations, such as busy transit stops, so that drivers can drop off packages without going all the way to your doorstep. When you’re ready to pick up your items, you just stop by at a time that’s convenient for you. This reduces both delivery truck mileage and the risk of packages being stolen off of porches.
  • Cargo bicycles: Companies can take the delivery truck out of the equation and use electric cargo bicycles to drop off smaller packages. In addition to being zero-emission, cargo bicycles are relatively inexpensive and easy to park, and they provide a healthier alternative for delivery workers.

To learn more about supply chains and delivery logistics, check with your town or city’s transportation department to see if they are testing or already have goods delivery programs or policies, like those in New York and Seattle. And the next time you order something for delivery, consider your options for receiving it, such as walking or biking to a package locker or pickup point, or consolidating your items into a single delivery.

Package delivery can be both convenient and sustainable if companies keep evolving their supply chains, and everyone thinks about how they want delivery to work in their neighborhoods.

Recommended Citation:
Goodchild, A. How many Amazon packages get delivered each year? The Conversation.

Ecommerce and Logistics Sprawl: A Spatial Exploration of Last-Mile Logistics Platforms

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Publication: Journal of Transport Geography
Volume: 112
Publication Date: 2023

The rise of ecommerce helped fuel consumer appetite for quick home deliveries. One consequence has been the placing of some logistics facilities in proximity to denser consumer markets. The trend departs from prevailing discussion on “logistics sprawl,” or the proliferation of warehousing into the urban periphery. This study spatially and statistically explores the facility- and region-level dimensions that characterize the centrality of ecommerce logistics platforms. Analyzing 910 operational Amazon logistics platforms in 89 U.S. metropolitan statistical areas (MSAs) between 2013 and 2021, this study estimates temporal changes in distances to relative, population centroids and population-weighted market densities. Results reveal that although some platforms serving last-mile deliveries are located closer to consumers than upstream distribution platforms to better fulfill time demands, centrality varies due to facility operating characteristics, market size, and when the platform opened.

Ecommerce has transformed the “consumption geography” of cities. These transformations have major implications for shopping behaviors and retail channels, last-mile operations and delivery mode choice, the management and pricing of competing uses for street and curb space, and the spatial ordering and functional role of logistics land uses. In the latter case, researchers have observed a diversification of logistics platforms to more efficiently serve home delivery demand. These platforms range from “dark stores” and “microfullfilment centers” that fulfill on-demand deliveries and omni-channeled retail without a consumer facing storefront, multi-use urban distribution centers that convert unproductive sites (e.g., abandoned rail depots) to more lucrative land uses, and “microhubs” that stage transloading between cargo vans and e-bicycles suited for dense urban neighborhoods.

Logistics spaces play an important role in improving urban livability and environmental sustainability. Planning decisions scale geographically from the region-level to the curb. Facilities such as urban consolidation centers and loading zones can mitigate common delivery inefficiencies, such as low delivery densities and “cruising” for parking, respectively. These inefficiencies generate many negative externalities including climate emissions, air and noise pollution, congestion, and heightened collision risks, especially for vulnerable road users such as pedestrians and bicyclists. Limited commercial data has made it difficult, however, to observe spatial patterns with regards to ecommerce logistics platforms.

Using detailed proprietary data, this paper explores the evolving spatial organization of ecommerce logistics platforms. Given the company’s preeminence as the leading online retailer in the U.S., the paper presents Amazon as a case study for understanding warehousing and distribution (W&D) activity in the larger ecommerce space. Utilizing proprietary data on Amazon logistics facilities between 2013 and 2021, this research explores the geographic shape and explanatory dimensions of ecommerce within major U.S. metropolitan areas. In the following section, this study defines the state of research related to broader W&D land use and its implications to ecommerce’s distinct consumption geography. Afterwards, two methodologies for measuring logistics centrality are tested: a temporally relative barycenter-based metric, the prevailing method in literature, and another GIS-based, population-weighted service distance metric. The two measurements reveal nuances between facility- and region-level differences in the spatial organization of ecommerce platforms, which has yet to be fully researched. After presenting results from an exploratory regression analysis, this study discusses implications for future urban logistics land use and transport decisions.

Recommended Citation:
Fried, T., & Goodchild, A. (2023). E-commerce and logistics sprawl: A spatial exploration of last-mile logistics platforms. Journal of Transport Geography, 112, 103692.

Urban Freight in 2030

There are many questions to answer about the future of urban delivery. How changes and developments in the industry will ultimately play out cannot really be predicted, but the Urban Freight Lab, a group of experienced professionals with deep and up-to-date knowledge of their subject, representing a broad range of urban freight stakeholders is best suited to envision the future. The Urban Freight in 2030 project will explore emerging urban freight trends, their impacts on local and global sustainable development, and propose Urban Freight Lab’s future course of action.

Objective: This project proposes to use the expertise of the Urban Freight Lab members and partners, supported by up-to-date research and subject specialists, to create a shared vision of the future of urban delivery in 2030. The work will produce vision documents to be shared publicly, outlining and detailing the Urban Freight Lab’s vision of the future of urban freight.

Summary of Project Tasks:

Task 1: Generate a candidate list of influential variables.

Task 2: UFL members provide feedback and democratically select four variables for future discussion.

Task 3: Schedule one category of variables discussion at each Urban Freight Lab quarterly meeting.

Task 4: Based on the discussions described in Task 3, UFL staff draft a number of public-facing documents that lay out our shared vision for Urban Freight 2030. The format of these products will be discussed during the course of the project.

Task 5: UFL members will review and revise the vision documents. When all members agree, it will be distributed publicly as a joint publication of the UFL research team and membership.


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

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Publication Date: 2022

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.

Modeling the Competing Demands of Carriers, Building Managers, and Urban Planners to Identify Balanced Solutions for Allocating Building and Parking Resources

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Publication: Transportation Research Interdisciplinary Perspectives
Volume: 15
Publication Date: 2022

While the number of deliveries has been increasing rapidly, infrastructure such as parking and building configurations has changed less quickly, given limited space and funds. This may lead to an imbalance between supply and demand, preventing the current resources from meeting the future needs of urban freight activities.

This study aimed to discover the future delivery rates that would overflow the current delivery systems and find the optimal number of resources. To achieve this objective, we introduced a multi-objective, simulation-based optimization model to define the complex freight delivery cost relationships among delivery workers, building managers, and city planners, based on the real-world observations of the final 50 feet of urban freight activities at an office building in downtown Seattle, Washington, U.S.A.

Our discrete-event simulation model with increasing delivery arrival rates showed an inverse relationship in costs between delivery workers and building managers, while the cost of city planners decreased up to ten deliveries/h and then increased until 18 deliveries/h, at which point costs increased for all three parties and overflew the current building and parking resources. The optimal numbers of resources that would minimize the costs for all three parties were then explored by a non-dominated sorting genetic algorithm (NSGA-2) and a multi-objective, evolutionary algorithm based on decomposition (MOEA/D).

Our study sheds new light on a data-driven approach for determining the best combination of resources that would help the three entities work as a team to better prepare for the future demand for urban goods deliveries.

Authors: Haena KimDr. Anne Goodchild, Linda Boyle
Recommended Citation:
Kim, H., Goodchild, A., & Boyle, L. N. (2022). Modeling The Competing Demands Of Carriers, Building Managers, And Urban Planners To Identify Balanced Solutions For Allocating Building And Parking Resources. In Transportation Research Interdisciplinary Perspectives (Vol. 15, p. 100656). Elsevier BV.

Commercial Vehicle Driver Behaviors and Decision Making: Lessons Learned from Urban Ridealongs

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Publication:  Transportation Research Record: Journal of the Transportation Research Board
Volume: 2675 (9)
Pages: 608-619
Publication Date: 2021

As ecommerce and urban deliveries spike, cities grapple with managing urban freight more actively. To manage urban deliveries effectively, city planners and policy makers need to better understand driver behaviors and the challenges they experience in making deliveries.

In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles with global positioning system devices.

The results showed that, on average, urban CVs spent 80% of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with only less than 5% of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times.

We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and competition with other commercial drivers.

The results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.

In recent years, cities have changed their approach toward managing urban freight vehicles. Passive regulations, such as limiting delivery vehicles’ road and curb use to given time windows or areas have been replaced by active management through designing policies for deploying more commercial vehicle (CV) load zones, pay-per-use load zone pricing, curb reservations, and parking information systems. The goal is to reduce the negative externalities produced by urban freight vehicles, such as noise and emissions, traffic congestion, and unauthorized parking, while guaranteeing goods flow in dense urban areas. To accomplish this goal, planners need to have an understanding of the fundamental parking decision-making process and behaviors of CV drivers.

Two main difficulties are encountered when CV driver behaviors are analyzed. First, freight movement in urban areas is a very heterogeneous phenomenon. Drivers face numerous challenges and have to adopt different travel and parking behaviors to navigate the complex urban network and perform deliveries and pick-ups. Therefore, researchers and policy makers find it harder to identify common behaviors and responses to policy actions for freight vehicles than for passenger vehicles. Second, there is a lack of available data. Most data on CV movements are collected by private carriers, who use them to make business decisions and therefore rarely release them to the public. Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions.

The urban freight literature discusses research that has employed various data collection techniques to study CV driver behaviors. Cherrett et al. reviewed 30 UK surveys on urban delivery activity and performed empirical analyses on delivery rates, time-of-day choice, types of vehicles used to perform deliveries, and dwell time distribution, among others. The surveys reviewed were mostly establishment-based, capturing driver behaviors at specific locations and times of the day. Allen et al. performed a more comprehensive investigation, reviewing different survey techniques used to study urban freight activities, including driver surveys, field observations, vehicle trip diaries, and global positioning system (GPS) traces. Driver surveys collect data on driver activities and are usually performed through in-person interviews with drivers outside their working hours or at roadside at specific locations. In-person interviews provide valuable insights into driver choices and decisions but are often limited by the locations at which the interviews occur or might not reflect actual choices because they are done outside the driver work context. Vehicle trip diaries involve drivers recording their daily activities while field observations entail observing driver activities at specific locations and establishments; neither collects insights into the challenges that drivers face during their trips and how they make certain decisions. The same limitations hold true for data collected through GPS traces. Allen et al. mentioned the collection of travel diaries by surveyors traveling in vehicles with drivers performing deliveries and pick-ups as another data collection technique that could provide useful insights into how deliveries/pick-ups are performed. However, they acknowledged that collecting this type of data is cumbersome because of the difficulty of obtaining permission from carriers and the large effort needed to coordinate data collection.

This study aims to fill that gap by collecting data on driver decision-making behaviors through observations made while riding along with CV drivers. A systematic approach was taken to observe and collect data on last-mile deliveries, combining both qualitative observations and quantitative data from GPS traces. The ridealongs were performed with various delivery companies in Seattle, Washington, covering a range of vehicle types (cars, vans, and trucks), goods types (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail).

The data collected will not only add to the existing literature by providing estimates of trip times, parking choice types, time and distance spent cruising for parking, and parking dwell times but will also provide insights into why those decisions are made and the factors affecting driver choices.

The objectives of this study are to provide a better understanding of CV driver behaviors and to identify common and unique challenges they experience in performing the last mile. These findings will help city planners, policy makers, and delivery companies work together better to address those challenges and improve urban delivery efficiency.

The next section of this paper describes the relevant literature on empirical urban freight behavior studies. The following section then introduces the ridealongs performed and the data collection methods employed. Next, analysis of the data and qualitative observations from the ridealongs are described, and the results are discussed in five overarching categories: the time spent in and out of the vehicle, parking location choice, the reasons behind those choices, parking cruising time, and factors affecting dwell time.

Recommended Citation:
Chiara, Giacomo Dalla, Krutein, Klaas Fiete, Ranjbari, Andisheh, & Goodchild, Anne. (2021). Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making. Transportation Research Record, 2675(9), 608-619.
Technical Report

Year Two 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

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Publication: U.S. Department of Energy
Publication Date: 2021

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:

  1. 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
  2. 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
  3. 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 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.

In Year 2, the team executed the implementation plan:

  • oversaw installation of the in-road sensors, and collecting and processing data,
  • managed installation, marketing and operations of three common locker systems in the pilot test area,
  • tested the prototype smart phone parking app with initial data stream, and
  • developed a truck parking behavior simulation model.
Recommended Citation:
Urban Freight Lab (2021). Year Two Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.