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Biking for Goods: A Case Study on the Seattle Pedaling Relief Project

1. Introduction
One of the disruptions brought by the COVID-19 pandemic was the reduction of in-store shopping, and the consequent increase in online shopping and home deliveries. However, not everyone had equal access to online shopping and home-delivery services. Customers relying on food banks were forced to shop in-store even during the pandemic. In 2020, the Cascade Bicycle Club started the Pedaling Relief Project (PRP) – a not-for-profit home delivery service run by volunteers using bikes to pick up food at food banks and deliver to food bank customers, among other services.

The Urban Freight Lab collaborates with the Cascade Bicycle Club (CBC) to study and improve PRP operations. For this work, students in Prof. Anne Goodchild’s Transportation Engineering course on Transportation Logistics (CET 587) are undertaking a case study: to analyze the transport and logistics system of the Pedaling Relief Project and provide recommendations for how to improve operations.

2. Background
2.1. Food rescue at a glance
An estimated 94,500 tons of food from Seattle business establishments end up in compost and landfills each year, while many members of our community remain food insecure. The process of food rescuing consists of the gleaning of edible food from business establishments – called donor businesses such as grocery stores, restaurants, and commissary kitchens – that otherwise would enter the waste stream and be re-distributed to local food programs. Hunger relief agencies, also referred to as food banks, are non-profit organizations that collect rescued food, either directly from businesses or through food rescue distributors (such as Food Lifeline or Northeast Harvest) and re-distribute it to the community through meal programs, walk-ins, and pop-up food pantries, student backpack programs, among others.

Read more about the Seattle food rescue system in SCTL’s report (2020) on “Improving Food Rescue in Seattle: What Can Be Learned from a Supply Chain View?

2.2. Pedaling Relief Project
In 2020 the Cascade Bicycle Club started the Pedaling Relief Project (PRP), a volunteer-based program that collaborates with local food banks to offer three main types of services — (1) grocery delivery, (2) food rescue, (3) little free pantry restocking — coordinating a network of volunteers on bikes.

  1. Grocery delivery (GD) service consists of picking up grocery bags from food banks and performing delivery routes, distributing food to food bank customers that asked for home delivery services.
  2. Food rescue (FR) services support the existing distributors by picking up food at business establishments and carrying rescued food to local food banks.
  3. Little free pantries restocking (LFPR) services consist of picking up food at local food banks and carrying it to neighborhood micro pantries –containers placed on local streets and open to everyone to store food from donors to whoever needs it. Learn more about the Little free pantries project on

Volunteers use their own bikes, with some cargo carry capacity, or can request a bike trailer or cargo bike from the Cascade Bicycle Club.

2.3. Cargo Bikes
Cargo bikes are two/three/four-wheel bikes with some cargo-carrying capacity. They are increasingly used as an alternative mode to trucks and vans to transport goods in urban areas. Cargo bikes are often supported by an electric motor that assists the driver when pedaling. Compared to internal combustion engine vehicles, cargo bikes do not produce tailpipe emissions and they consume less energy than electric vans (Verlinghieri et al., 2021). They also offer several operational advantages: they are more agile in navigating urban road traffic, they can use alternative road infrastructure such as bike lanes and sidewalks to drive and park, they can park closer to their delivery destination, reducing walking distances and parking dwell times (Dalla Chiara et al., 2020).

3. Project instructions

The CBC provided access to anonymous data on the PRP operations for the exclusive use of the 2022 CET 587 course student cohort final projects. Students are asked to individually perform empirical research using the provided data and/or self-collected data on the PRP operations with the following objectives:

  • Empirically analyze and describe PRP operations.
  • Provide recommendations on what actions can be taken to improve PRP operations.

Projects will meet the following two requirements:

  • Use the provided data and/or self-collected and/or publicly sourced data to perform empirical analysis
  • Provide justified and concrete recommendations on how to improve the PRP.
  • Complete deliverables 1 and 2 (see below), which consist of 2 presentations, a project proposal, and a final project report.

Project progress timeline and deliverables:

Weeks Progress & Deliverables
1-2 Become familiar with R language programming; PRP background and data
3 CBC gives a guest lecture about PRP
4-5 Project proposal; 2-minute lightning talk about the project proposal
Deliverable 1: 1-page project proposal
6-10 Implement proposed methodology and perform research
11 Each student will give a 15-minute presentation of the main results of the project
Deliverable 2: Final report
The following are potential project directions:
  • Analyze current routes performed by volunteers. How can they be improved? Get the work done more quickly, or with fewer bikes?
  • Analyze data from little free pantries restocking. Collect additional data on the use of Little Free Pantries by manual observations or by installing sensors in a few of them. Can we model demand and supply for food donations?
  • Collect and analyze GPS data by signing up and performing some of the PRP routes yourself. What type of infrastructure do cargo bikes need and how does street and curb use behavior differ between cargo bikes and vans? What can the city do to better support this type of activity?
  • Analyze volunteers’ behaviors data. Is it possible to model the supply of volunteers? Can you simulate different scenarios of volunteer supply?
  • Develop your own direction with approval.

Students will be provided with a base dataset on PRP operations. Students are encouraged to use other datasets self-collected or from public data sources (e.g. check out the SDOT Open Data Portal), to share ideas in class and among each other, to use as much as possible class time, guest lectures and office hours to ask questions and share ideas.

1: 1-page project proposal and 2-minute lightning talk describing motivation, project objective(s) and research question(s), proposed methodology (data to use/collect, methods to implement), and expected results.

2: Final report and 10-minute presentation describing data used, including sample size and sample statistics, how data collection was performed, empirical analysis performed using data and results from the analysis, and conclusions, key findings, and key recommendations.

Optimization of Supply and Transportation Networks in an Epidemic Situation in Collaboration with the Seattle Flu Study

The mission of the Seattle Flu Study (SFS) is to prototype city-scale capabilities for epidemic preparedness and response. One of the aims of this study is to understand methods to implement rapid interventions outside of clinical settings and within 48-72 hours of the onset of symptoms, to enable the immediate diagnosis, treatment, or isolation of flu-positive individuals.

SFS has reached out to the Supply Chain Transportation and Logistics Center at the University of Washington to test various to develop models and perform sensitivity analyses on epidemic response scenarios via simulation and mathematical optimization. Modeling will allow SFS to measure and understand questions like, “when will our supply chain break?”, “how do you prevent it from breaking?” and “how do you get drugs and tests to people if your driver workforce gets sick?”. By modeling these types of scenarios, they will be able to assess the pros and cons of various supply chain strategies and develop multiple levers that can be pulled depending on the epidemic situation including prepositioning of orders, and leveraging in-house and supplementary private transportation alternatives (FedEx, etc.).

Common Microhub (Seattle Neighborhood Delivery Hub)


The importance of efficient urban logistics has never been greater. The response to COVID-19 has put new constraints and demands on the urban freight system but also highlighted the essential and critical nature of delivery and distribution. New requirements for reducing human contact only add weight to many of the strategies such as neighborhood kitchens, locker deliveries, and autonomous driverless delivery vehicles, already envisioned before the coronavirus pandemic. Social distancing and virus vector management also add new requirements and metrics for evaluating and managing logistics that are catalyzing innovation and motivating change in the urban logistics space.

What is a Common Microhub?

Also known as an urban consolidation center or a delivery transfer point, a microhub is a central drop-off/pick-up location for goods and services, which can be used by multiple delivery providers, retailers, and consumers. Microhubs can reduce energy consumption, noise pollution, congestion, and cost, and increase access, sustainability, and livability in cities, by allowing the final mile of delivery to be shifted to low-emission vehicles or soft transportation modes (cargo bike or walking), In addition to allowing for consolidation or deconsolidation of shipments, the design also enables neighbors to engage with additional services.

Microhubs provide:

  • access points for shared mobility
  • touchless pick-up and drop-off points
  • a home base for zero-emissions last-mile delivery, autonomous, and modalities
  • a shared public space
  • charging infrastructure
  • increased delivery density, reducing traffic and delivery vehicle dwell time
  • trip chaining capability

Urban Freight Lab’s Common Microhub Pilot: The Seattle Neighborhood Delivery Hub

The Urban Freight Lab’s Common Microhub project—the Seattle Neighborhood Delivery Hub—provides an opportunity for members to test and evaluate urban logistics strategies on the ground in Seattle’s Uptown neighborhood. As third-party logistics companies entering the last-mile space and more cities committing to environmental focus and zero-emissions vision, the interest in creating logistics places in urban proximity is growing. The outcomes of this research can guide the development of future microhub implementations in other cities. Participating stakeholders, while collaborative, operate with relative independence within the hub space. Data collection and analysis are ongoing; key indicators being measured include both operator performance and expected local impacts. In addition, lessons learned are encountered continuously and shared with UFL members as the project progresses.

Participants and Products

Product: Common Carrier Parcel Lockers
Host: Urban Freight LabDescription: The Urban Freight Lab is operating a common carrier parcel locker — a secure, automated, self-service storage system designed to accommodate deliveries from multiple transportation providers delivering a range of parcel sizes and open to all neighbors and commuters. Such lockers create delivery density, enabling vehicles to transport many packages to a single stop, rather than making multiple trips to accomplish the same task. This new approach reduces dwell time and failed first deliveries, both of which produce congestion and emissions, and increase costs. During the COVID-19 pandemic, the lockers also provide a no-contact solution for customers.

REEF neighborhood kitchen

Product: Neighborhood Kitchen and Infrastructure
Host: REEF

Description: Neighborhood kitchens are non-customer-facing modular vessels where food is prepared for mobile app or delivery orders. Removing front-of-house operations reduces a restaurant’s footprint, increases sustainability, and gives food entrepreneurs a platform by reducing overhead costs.

REEF is also the infrastructure partner, leveraging their parking lot holdings for the Seattle Neighborhood Delivery Hub location.

Coaster Cycles bike

Product: Electric-Assist Cargo Bike Fleet
Host: ​​Coaster Cycles

Description: Montana-based Coaster Cycles is providing an electric-assist cargo trikes fleet. These trikes are customized to carry BrightDrop EP1s, providing an agile, sustainable last-mile delivery solution in dense urban areas, reducing the emissions, congestion, and noise produced by traditional truck delivery.
(Watch the Coaster Cycle / EP1 deployment:

Screenshot of Axlehire app

Product: Last-Mile Delivery Routing Software

Description: Berkeley-based logistics startup Axlehire provides last-mile delivery routing software that creates the fastest, most efficient routes possible. AxleHire is using the Seattle Neighborhood Delivery Hub site as a transshipment point, where trucks will transfer packages transported from a suburban depot to smaller, more nimble Coaster Cycle electrically-assisted bicycles, which are driven by Axlehire operators to a final customer.

Brightdrop's EP1 electric pallet

Product: Electric Pallet (EP1)
Host: ​BrightDrop (General Motors)

Description: BrightDrop (a subsidiary of General Motors) focuses on electrifying and improving the delivery of goods and services. BrightDrop’s first product to market is the EP1, a propulsion-assisted electric pallet designed to easily move goods over short distances. Because the pallet is electric-powered, it supports sustainability efforts, improves driver safety and freight security, lowers labor costs, and reduces errors and package touches.

Product: MUST Devices and Data Collection
Host: University of Washington Smart Transportation Application & Research (STAR) Lab

Description: To assess performance, researchers have deployed a multitude of sensors, including STAR Lab’s Mobile Unit for Sensing Traffic (MUST) sensors, cameras with vehicle recognition technology, GPS tracking sensors, and parking occupancy sensors. Researchers can gain a comprehensive understanding of delivery operations (such as miles traveled, infrastructure usage, speed, battery usage, interaction with other vehicles, bikes, and pedestrians) and activities at the site itself (such as parking occupancy, duration and, mode distribution of vehicle types at the site).


The Seattle Neighborhood Delivery Hub is located at 130 5th Ave. N. in Seattle’s Uptown neighborhood.


The goals of the Common Microhub Research Project are to:

    1. Conduct a research scan of published reports that provide data-based evidence of the results of projects that have elements that are similar to Common Microhubs.
    2. Identify and characterize informal microhub activities observed in cities worldwide.
    3. Solicit input from UFL members as to the perceived benefits of microhubs and  the desired physical characteristics of a microhub
    4. Compare and contrast the priorities of UFL members with established metrics in the literature.
    5. Seek agreement from UFL members as to the microhub characteristics and location that would be feasible and desirable to operate in the Seattle region. Priority will be given to current UFL members, but should a third party external to UFL be necessary to run the microhub, proposals to host the microhub would be sought.
    6. Collect and analyze field data to measure both operator performance (including VMT, parking demand, fuel, and energy consumption) and expected local impacts (including travel and parking activity) before and after implementation. Data collection will rely on VMT, GPS, and travel time sources where available, but we expect to develop and implement customized methods to collect additional traffic and travel time data as needed. We may also interview the microhub operator and users to obtain qualitative data on the operations. The following tasks will be completed by the Urban Freight Lab in the two-year project.

Project Tasks

The following tasks will be completed by the Urban Freight Lab in the two-year project.

Task 1: Research Scan


  1. Conduct a research scan of published reports that provide data-based evidence of the results of projects that have elements that are similar to Common MicroHubs.
  2. Identify and characterize informal microhub activities observed in cities worldwide.
  3. Write a summary of the results.

Task 2: Develop MicroHub Priorities


  1. Solicit input from UFL members as to:
    • the perceived benefits of microhubs
    • the desired physical characteristics of a microhub
  2. Compare and contrast the priorities of UFL members with priorities demonstrated in the literature.

Task 3: Select Operator and Define Operational Model


  1. With the help of a microhub operator, seek agreement from UFL members as to the microhub characteristics, services, operational goals and location that would be feasible and desirable to operate in the Seattle region.
    • Priority will be given to current UFL members to operate the Hub, but should a third party external to UFL be necessary to run the microhub, proposals to host the microhub would be sought.
  2. Go/No Go decision by researchers, UFL members, and microhub operator as to whether a pilot test will move forward.
    • Sufficient interest amongst participating UFL members and an understanding of the operating model and participants’ business objectives will be necessary to move forward as per the operator’s approval.
    • The operator will work independently with participants and/or the University of Washington to establish operating model(s) under separate agreement(s).

Task 4: Select Operator and Define Operational Model


  1. Define key metrics for evaluation and data collection plan.
  2. With the support of UFL members participating in the pilot, collect “before” data to contrast with data collected during pilot operations.

Task 5: Implementation


  1. Support the implementation of a microhub with UFL partners that have agreed to the terms of the pilot.
  2. Project schedule will allow for 6 months of operations, followed by 3 months for analysis.
  3. Collect and analyze field data to measure both operator performance (including VMT, parking demand, fuel, and energy consumption) and expected local impacts (including travel and parking activity) after implementation. Data collection will rely on VMT, GPS, and travel time sources where available, but we expect to develop and implement customized methods to collect additional traffic and travel time data as needed. We may also interview the operator and users to obtain qualitative data on the operations.

Task 6: Evaluate Operations


  1. Provide progress reports at quarterly UFL meetings.
  2. Final report with key project findings.

COVID-19 Impacts on Online and In-Store Shopping Behaviors: Why they Happened and Whether they Will Last Post Pandemic

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Publication:  Transportation Research Record: Journal of the Transportation Research Board
Publication Date: 2023

Throughout the COVID-19 pandemic, online and in-store shopping behaviors changed significantly. As the pandemic subsides, key questions are why those changes happened, whether they are expected to stay, and, if so, to what extent. We answered those questions by analyzing a quasi-longitudinal survey dataset of the Puget Sound residents (Washington, U.S.). We deployed structural equation modeling (SEM) to build separate models for food, grocery, and other items shopping to explore the factors affecting such changes. The results revealed that people’s online and in-store shopping frequencies during the pandemic were affected by their perceived health risks, attitudes toward shopping, and pre-pandemic shopping frequencies. Similarly, it was shown that how frequently people expect to shop post pandemic is influenced by their attitudes toward shopping, changes during the pandemic, and their pre-pandemic frequencies. We also classified respondents into five groups, based on their current and expected future shopping behavior changes, and performed a descriptive analysis. The five groups—Increasers, Decreasers, Steady Users, Returnees, and Future Changers—exhibited different trends across online and in-store activities for shopping different goods. The analysis results showed that, while 25% of the respondents increased their online shopping, only 8% to 13% decreased their in-store activities, implying that online shopping did not completely substitute in-store shopping. Moreover, we found that online shopping is a substitution for in-store shopping for groceries, while it complements in-store shopping for food and other items. Additionally, more than 75% of new online shoppers expect to keep purchasing online, while 63%–85% of in-store Decreasers plan to return to their pre-pandemic frequencies.

The rise of e-commerce, busy lifestyles, and the convenience of next- and same-day home deliveries have resulted in exponential growth of online shopping in the U.S., rising from 5% of the total retail in 2011 to 15% in 2020, and it is expected to grow even further in the future. Worldwide, spending on e-commerce passed $4.9 trillion in 2021 and it is projected to surge to $7 trillion by 2025.

In the past few years, there has been ongoing research on how this growth would change people’s travel patterns and whether its effect on in-person activities would be substitution, complementing, or modification. However, there is no single answer to this question, given different product types, regions, demographics, and primary travel modes.

While online purchasing had already been experiencing a growth every year before 2020, the pandemic accelerated this trend. In 2020, online shopping constituted more than 20% of total spending on consumer goods worldwide in comparison to 16.4% in 2019 and 14.4% in 2018. Before COVID-19, it was predicted that total e-commerce sales in the U.S. would grow up to $674.88 billion, yet the actual number turned out to be $799.18 billion. With a 15.9% growth, the U.S. is among the top 10 countries with the highest growth rate in online retail shopping in 2022.

Embracing digital technologies and bringing shops into homes are among the immediate impacts of the pandemic restrictions and lockdowns, with the majority of people reducing their frequency of going to stores and adopting alternative shopping approaches such as curbside pick-up and home delivery. Based on the reports by the U.S. Bureau of Transportation Statistics (BTS), in Nov–Dec 2020, when the penetration of the coronavirus reached its first peak in the U.S., the percentage of people who decided to shop online instead of going to stores increased by up to 10%. During the early pandemic, about 35% of U.S. workers switched to remote working, and from March to April 2020, the average daily number of people staying home increased by 32 million and the total number of trips decreased by 2.5B. Dining-in restaurants were also banned in half of the U.S. states for several months in 2020, which resulted in a significant drop in the restaurant dine-in demand and shifted people toward online food delivery services, and buying groceries online rather than going to store.

These changes were also influenced by socio-demographic characteristics. For instance, according to the BTS, the percentage of people with an annual income close to $125,000 who replaced their in-store shopping by online shopping in Nov–Dec 2020 was twice those with an annual income of $25,000. People in the neighborhoods with higher number of positive COVID-19 cases or higher spread rate of positive new cases were more likely to change their in-store shopping to online-shopping. Senior people were also shown to have higher tendency to shop online compared with younger generations, perhaps because of health and safety concerns. It is worth noting that these changes were not the same across all products; for example, online sales of food and beverage in the U.S. doubled in 2020, while home furniture online sales only increased by about 50%.

Another factor that is proved to have a major effect on people’s shopping behaviors and travel patterns during the pandemic is their risk perception and fears for their health. Irawan et al. found that perceiving COVID-19 as a severe disease decreased people’s tendency to do in-store grocery shopping. Similarly, Moon et al. found out that, during the pandemic, people who considered themselves less vulnerable to the infection were less likely to use online channels for shopping. Several studies have mentioned that the perceived health risk varies among different groups of population and depends on region, age, gender, education, race, and marital status.

Moreover, people’s online and in-store shopping behaviors are affected by their socio-demographic factors and their attitudes toward the activity. The advantages and disadvantages of online shopping over in-store shopping play a role in attitudes toward the activity. The advantages, such as receiving goods without leaving home, having access to a wider variety of products and information, and being able to compare them easily and efficiently, result in a positive attitude toward online shopping, especially during the pandemic given high perceived health risk, formal penalties, or both. On the other hand, online shopping has some disadvantages, such as transaction security concerns and long delivery times, and in-store shopping offers specific benefits, such as the ability to see, touch, feel, and try the products, ensuring the store’s environment quality, immediate possession of the product, social interaction, and entertainment. Therefore, even during the pandemic, some people maintained frequent in-store shopping trips.

Whether the pandemic-induced changes in online and in-store shopping are permanent is still debatable. Sheth discussed that people may find the new routine more convenient, affordable, and accessible, and therefore stick to it even after the pandemic is over. On the contrary, Dannenberg et al. argued that people’s motives to shop online only hold for the time of crisis, and online retailing will decline when circumstances change. Watanabe and Omori showed that most people used to shop online long before the pandemic, and they merely increased their frequency because of infection risk. So, the reasons behind the surge in online shopping might dissipate as COVID-19 recedes.

In this paper, we study how online and in-store shopping behaviors for different goods were affected during COVID-19, and whether those changes are expected to stay post pandemic. We analyze a quasi-longitudinal survey dataset from the Puget Sound region in Washington State, U.S., that includes data on people’s shopping behavior before and during pandemic, as well as their expected shopping behavior after pandemic. The dataset also contains information on socio-demographic characteristics, as well as psychometric questions about COVID-19 risk perception and attitudes toward shopping. Through descriptive analysis and structural equation modeling (SEM), we explore the factors that directly or indirectly affected people’s three shopping activities (online and in-store), for food, grocery, and other items (clothing, home goods, etc.), and investigate the similarities and differences amongst them.

This study is distinguished in several ways from the previous ones that investigated the impacts of COVID-19 on people’s shopping behavior: (1) it applies a unique descriptive analysis by classifying respondents based on their current and expected future shopping trends and studies how socio-demographic characteristics (directly and indirectly) influence people’s shopping behaviors by analyzing the similarities and differences between those groups; (2) it models online and in-store shopping jointly, considering covariations and dependencies between those two modes; (3) it applies the same methodology and set of variables to three different shopping activities (for food, grocery, and other items) and compares and contrasts their observed/expected trends and influencing factors; and (4) in addition to socio-demographic and attitudinal variables, it considers people’s baseline shopping behaviors (how frequently they shopped online and in-store before the pandemic) as factors affecting their expected post-pandemic shopping behaviors.

Authors: Dr. Andisheh Ranjbari, Jorge Manuel Diaz-Gutierrez (Pennsylvania State University, Helia Mohammadi-Mavi (Pennsylvania State University)
Recommended Citation:
Diaz-Gutierrez, J. M., Mohammadi-Mavi, H., & Ranjbari, A. (2023). COVID-19 Impacts on Online and In-Store Shopping Behaviors: Why they Happened and Whether they Will Last Post Pandemic. Transportation Research Record: Journal of the Transportation Research Board, 036119812311551. 

Analysis of Online Shopping and Shopping Travel Behaviors in West Seattle

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Publication Date: 2023

The purpose of this research is to explore consumers’ online shopping and in-person shopping travel behaviors and the factors affecting these behaviors within the geographical context of the study area of West Seattle.

West Seattle is a peninsula located southwest of downtown Seattle, Washington State. In March 2020, the West Seattle High Bridge, the main bridge connecting the peninsula to the rest of the city, was closed to traffic due to its increased rate of structural deterioration. The closure resulted in most of the traffic being re-distributed across other bridges, forcing many travelers to re-route their trips in and out of the peninsula. At about the same time, the COVID-19 pandemic caused business-shuttering lockdowns. Both events fundamentally changed the nature of shopping and the urban logistics system of the study area.

The Seattle Department of Transportation (SDOT) engaged the Urban Freight Lab (UFL) at the University of Washington to conduct research to understand current freight movements and goods demands in West Seattle and identify challenges related to the bridge closure to inform data-driven mitigation strategies. The study took place in two phases: the first phase documented the challenges experienced by local businesses and carriers through a series of interviews and quantified the freight trip generated by land use in the case study area1 ; the second phase, described in the current report, performed an online survey to understand online shopping and in-person shopping travel behaviors for West Seattle residents.

The main objectives of the current study are twofold:

  • Describe online shopping and shopping travel consumer behaviors for West Seattle residents.
  • Understand what factors influence consumer shopping behaviors, from accessibility to local stores, to the characteristics of goods purchased, to socio-economic factors.


To address these objectives, the research team designed an online questionnaire that was advertised through a West Seattle Bridge Closure-related SDOT newsletter and other local online media outlets during the spring and summer of 2022. The questionnaire asked respondents about their socioeconomic conditions (age, income, education, etc.), where they live and their access to transportation (vehicle ownership and types of vehicles), their online shopping behavior, the impact of the West Seattle High Bridge closure on their shopping habits, and about their most recent purchase for a given category of goods among clothing items, groceries, restaurant food, and household supplies. The questionnaire was collected anonymously, and no personally identifiable information was collected. A total of 1,262 responses were collected, and after data processing, the final sample data consisted of 919 responses, corresponding approximately to 1 percent of the study area population.

Comparing the socioeconomic characteristics of the sample with those of the West Seattle study population it should be noted that individuals identifying themselves as white and female and of older age were oversampled, while individuals with lower than a college degree and with annual income less than $50,000 were under-sampled. Therefore, the sample in general is more representative of a more affluent, older population.

Key Findings

The key findings are summarized as follows:

Online shopping is widespread for clothing items and restaurant food.

Respondents receive on average 5 deliveries per week, across all goods categories. 38.7 percent of the respondents reported performing their most recent shopping activity online, considering all goods categories. However, the frequency of online shopping varied across different goods categories. Most of the respondents that purchased groceries or household supplies reported having shopped in person (89 and 75 percent of the respondents respectively), while, in contrast, for those that purchased restaurant food and clothing items, two-thirds of respondents reported buying online in both categories. Online shopping is widespread in the clothing and restaurant food markets, but less in grocery and household supplies markets. Of the consumers that shopped online for restaurant food, 76 percent of them decided to travel to take out (also referred to as curbside pickup), and only 24 percent of them chose to have the meal delivered directly to their home.

Online shopping is more widespread among mobility-impaired individuals

Participants were asked whether they had a disability that limited physical activities such as carrying, walking, lifting, etc. Of the 918 participants, 98 (11%) responded that they did have a disability that fit this description. The share of respondents that shop online was higher among mobility-impaired individuals (30 percent online for delivery and 19 percent online for pick-up) compared to individuals that did not report any mobility impairment (23 percent online for delivery and 12 percent online for pick-up).

Driving is the predominant shopping travel mode

Of the sample of respondents, 96 percent reported having access to a motorized vehicle within their household. Driving is also the most common shopping mode of in-person travel, with 81.3 percent of respondents reporting that they drove to a store to shop. Walking is a distant second preferred shopping travel mode, with 13.1 percent of respondents reporting having walked to a store. Biking and public transit were rarely adopted as a shopping travel mode, together they were observed 5.6 percent of the time. Though included as a travel option, only 1 participant reported using a rideshare vehicle to shop.

Electrification in West Seattle

Of the respondents that have access to a motorized vehicle in their households, 9.8 percent of them reported owning an electric vehicle. Car ownership is much more widespread than bike ownership, with 51.6 percent of the respondents reporting having access to a bike. Among these, 15.5 percent of them said that at least one of their bikes is electric.

The 10-minute city

The average walking time across all types of goods purchased was 10 minutes. The average driving time, for those respondents that reported driving to a store, was also about 10 minutes, except for those who reported purchasing clothing items, which reported on average of 27-minute trip time (both using a private car or using public transit). The longest travel times are seen mostly for respondents that took public transit as a shopping travel mode.

Living in proximity to stores reduces driving and online deliveries

A higher number of stores within a 10-minute walking distance (0.5 miles) is correlated with a higher number of consumers choosing to walk to a store, compared to those that chose to drive to a store or that shopped online. This is true across all goods types, but it is more significantly seen in grocery shopping. Moreover, accessibility to commercial establishments at a walking distance has a stronger impact on reducing the likelihood of driving, and at a lesser magnitude, reduces the propensity of shopping online.

Delivery to the doorstep is the most common destination for online deliveries

For those that chose to buy online, the most common delivery destination was at the respondents’ home doorstep (84 percent of respondents reported receiving online deliveries at home). The second most frequently used delivery destination was parcel lockers (15 percent of respondents), with 12 percent of respondents making use of private lockers, while only 3 percent made use of public lockers. The remaining one percent received deliveries at other destinations (e.g. office or nearby store).

The West Seattle High Bridge closure incentivized local shopping

When asked about the impacts of the West Seattle Bridge closure on individual online and shopping travel behaviors, more respondents reported buying more locally and online, vs. traveling farther for shopping and buying in person.

Recommended Citation:
Goodchild, A., Dalla Chiara, G., Verma, R., Rula, K. (2023) Analysis of Online Shopping and Shopping Travel Behaviors in West Seattle, Urban Freight Lab.
Technical Report

Analyzing the Long-Term Impacts of COVID-19 Disruption on Travel Patterns

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Publication Date: 2020

The rapid spread of COVID-19 pandemic in the U.S. spurred many state governments to take extensive actions for social distancing and issue stay-at-home orders to reduce the spread of the virus. Washington State and all other States in the PacTrans region have issued stay-at-home orders that include school closures, telecommuting, bars/restaurants closures, and group gathering bans, among others. These actions create significant changes to daily life and while some travel patterns will gradually restore by the end of outbreak, some may remain changed for a much longer period.

Behaviors that may see a lasting response include commuting, grocery shopping, business meetings, and even social interactions. Working from home for 2-3 months may change people’s attitudes toward telecommuting, and some may continue to do so a few days a week once the stay-at-home orders are lifted. Some employers may also shift their telecommute policies and provide/encourage working from home. In recent years, with the growth of e-commerce, many grocery stores had started to offer home deliveries; however, online grocery shopping experienced a fast and sudden boom during the pandemic. This has resulted in quick service adoption, and therefore some people may continue to do online grocery shopping once things go back to normal. Moreover, as people shift to online grocery shopping, they may proactively make a list and place orders less frequently compared to them going to store, resulting in fewer shopping trips. Some business meetings and even personal gatherings may also move online as people learn about and try alternate ways of communicating during the outbreak. Some may also consider enrolling in distant learning programs instead of attending in-person educational programs. There may also be significant changes in modes of travel. Some transit commuters may choose other modes of transportation for a while, and people may choose to drive or bike instead of taking a ride-hailing trip.

The goal of this research is to understand how COVID-19 disruption has affected people’s activity and travel patterns during the pandemic, and how these changes may persist in a post-pandemic era.

Authors: Dr. Andisheh Ranjbari, Parastoo Jabbari, Don MacKenzie
Recommended Citation:
Mackenzie D., Jabbari P., Ranjbari A. Analyzing the Long-Term Impacts of COVID-19 Disruption on Travel Patterns. Pacific Northwest Transportation Consortium (PacTrans). 2020.
Technical Report

Impacts of COVID-19 on Supply Chains

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Publication Date: 2020

As of June 2020, the novel coronavirus disease (COVID-19) has infected more than eight million people worldwide. In response to the global pandemic, cities have been put under lockdown, closing non-essential businesses and banning group gatherings, limiting urban mobility, and issuing stay-at-home orders, while nations closed their borders.

During these times, logistics became more important than ever in guaranteeing the uninterrupted flow of goods to city residents. At the same time, the same supply chain providing the goods experienced profound disruptions. Documenting the impacts the COVID-19 outbreak had on individual organizations and their responses is an important research effort to better understand the resiliency of the supply chain.

The Urban Freight Lab, a structured workgroup of senior executives from major supply chains, supply chain related companies, and academic researchers from the University of Washington, carried out a survey to address two main questions:

  • What are the most common and significant impacts of the COVID-19 outbreak?
  • What short-term actions and long-terms plans are supply chains taking in response to the pandemic?


Recommended Citation:
Urban Freight Lab (2020). Impacts of COVID-19 on Supply Chains.