Research Topic: The Final 50 Feet of the Urban Goods Delivery System

The Urban Freight Lab coined the term “Final 50 Feet” and defined it as the supply chain segment that begins when a delivery vehicle pulls into a parking space and stops moving — in public load/unload spaces at the curb or in an alley, or a building’s loading dock or internal freight bay. It tracks the delivery process inside buildings and ends where the customer takes receipt of their goods. This research analyzes processes, develops potential solutions, and tests operational improvements in the final segment of the urban goods delivery system.

Common Microhub (Seattle Neighborhood Delivery Hub)

Background

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.
	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.
	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: https://vimeo.com/528552173)
	Product: Last-Mile Delivery Routing Software Host: AxleHire 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.
	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).

Location

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

Goals

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

Subtasks:

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.
Identify and characterize informal microhub activities observed in cities worldwide.
Write a summary of the results.

Task 2: Develop MicroHub Priorities

Subtasks:

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

Task 3: Select Operator and Define Operational Model

Subtasks:

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.
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

Subtasks:

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

Task 5: Implementation

Subtasks:

Support the implementation of a microhub with UFL partners that have agreed to the terms of the pilot.
Project schedule will allow for 6 months of operations, followed by 3 months for analysis.
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

Subtasks:

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

Paper

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

Download PDF (5.20 MB)

URL: https://doi.org/10.1016/j.trip.2022.100656

Publication: Transportation Research Interdisciplinary Perspectives

Volume: 15

Publication Date: 2022

Summary:

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. https://doi.org/10.1016/j.trip.2022.100656

Paper

Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making

Download PDF (1.85 MB)

URL: https://journals.sagepub.com/doi/pdf/10.1177/03611981211003575

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

Volume: 2675 (9)

Publication Date: 2021

Summary:

As e-commerce 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 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 (1), 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 policymakers 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 (2). Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions (3).

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. (5) 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. (5) 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.

Authors: Dr. Giacomo Dalla ChiaraFiete KruteinDr. Andisheh RanjbariDr. Anne Goodchild

Recommended Citation:
Dalla Chiara, G., Krutein, K. F., Ranjbari, A., & Goodchild, A. (2021). Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making. Transportation Research Record: Journal of the Transportation Research Board, 036119812110035. https://doi.org/10.1177/03611981211003575.

Paper

Bringing Alleys to Light: An Urban Freight Infrastructure Viewpoint

Download PDF (2.13 MB)

URL: https://doi.org/10.1016/j.cities.2020.102847

Publication: Cities

Volume: 105

Publication Date: 2020

Summary:

There is growing pressure in cities to unlock the potential of every public infrastructure element as density and demand for urban resources increase. Despite their historical role as providing access to land uses for freight and servicing, alleys have not been studied as a resource in modern freight access planning.

The authors developed a replicable data collection method to build and maintain an alley inventory and operations study focused on commercial vehicles. A Seattle Case study showed that 40% of the urban center city blocks have an alley. 90% of those alleys are wide enough to accommodate only a single lane for commercial vehicles. 437 parking operations were recorded in seven alleys during business hours and found that all alleys were vacant 50% of the time.

This confirms that, in its alleys, Seattle has a valuable resource as both space for freight load/unload; and direct access to parking facilities and business entrances for commercial, private, and emergency response vehicles.

However, alley design features and the prevalence of parking facilities accessed through the alley may restrict the freight load/unload space in the alley. Future efforts should investigate how to better manage these infrastructures.

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

Recommended Citation:
Machado-León, Girón-Valderrama, G. del C., & Goodchild, A. (2020). Bringing Alleys to Light: An Urban Freight Infrastructure Viewpoint. Cities, 105. https://doi.org/10.1016/j.cities.2020.102847

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

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

The Urban Freight Lab (UFL) received $1.5 million in funding from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (DOE EERE) to undertake work to help goods delivery drivers find a place to park without driving around the block in crowded cities for hours, wasting time and fuel and adding to congestion. The project partners will integrate sensor technologies, develop data platforms to process large data streams, and publish a prototype app to let delivery drivers 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.

The UFL will also pilot test common carrier locker systems in public and private load/unload spaces near transit stops. Transit riders, downtown workers, and residents will be able to pick up packages they ordered online from any retailer in a convenient and secure locker in a public plaza or outside their office. The benefits don’t stop there. Common carrier lockers create delivery density that increases the productivity of parking spaces and provides significant commercial efficiencies. They do this by reducing the amount of time it takes delivery people to complete their work. 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, it decreases the time their truck needs to be parked, increasing turnover and adding parking capacity in crowded cities.

This is a timely project as cities are looking for new strategies to accommodate the rapid growth of e-commerce. Online shopping has grown by 15% annually for the past 11 years, and is now 9% of total retail sales in the U.S., with $453.5 billion in revenue in 2017. Many online shoppers want the goods delivery system to bring them whatever they want, where they want it, in one to two hours. At the same time, many cities are replacing goods delivery load/unload spaces with transit and bike lanes. Cities need new load/unload space concepts supported by technology to make the leap to autonomous cars and trucks in the street, and autonomous freight vehicles in the Final 50 Feet of the goods delivery system. The Final 50 feet segment starts when a truck parks in a load/unload space, and includes delivery persons’ activities as they maneuver goods along sidewalks and into urban towers to make their deliveries.

The goals of this project are to:

Reduce parking seeking behavior by 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 area locations by 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems.
Increase network and commercial firms’ efficiency by increasing curb and alley space occupancy rates, and underutilized private loading bay occupancy in the p.m. peak, in the pilot test area.

Cost-share partnering organizations are:

Seattle Department of Transportation
Bellevue Department of Transportation
CBRE Seattle
King County Metro Transit
Kroger Company
Puget Sound Clean Air Agency
Sound Transit

Members of the UFL are also participating in the project. Pacific National National Laboratory (PNNL) is a partner, completing several of the project tasks.

Presentation

Roadblocks to Sustainable Urban Freight

Download Slides PDF (0.63 MB)

URL: https://www.metrans.org/inuf_2022-papers_presentations

Publication: 9th International Urban Freight Conference, Long Beach, May 2022

Publication Date: 2022

Summary:

While several stakeholders in the private and public sectors are taking actions and drafting roadmaps to achieve sustainable urban freight goals, the urban freight ecosystem is a complex network of stakeholders, achieving such sustainability goals requires the collaboration and coordination between multiple agents. Researchers collected and synthesized views from both the private and public sectors on what is needed to sustainably deliver the last mile and identify roadblocks towards this goal.

Authors: Thomas MaxnerDr. Giacomo Dalla ChiaraDr. Anne Goodchild

Recommended Citation:
Thomas Maxner, Giacomo Dalla Chiara, Anne Goodchild (2022). Roadblocks to Sustainable Freight. 9th International Urban Freight Conference (INUF), Long Beach, CA May 2022.

Empirical Analysis of Commercial Vehicle Dwell Times Around Freight-Attracting Urban Buildings in Downtown Seattle

Download PDF (4.24 MB)

URL: https://doi.org/10.1016/j.tra.2021.02.019

Publication: Transportation Research Part A: Policy and Practice

Volume: 147

Pages: 320-338

Publication Date: 2021

Summary:

This study aims to identify factors correlated with dwell time for commercial vehicles (the time that delivery workers spend performing out-of-vehicle activities while parked). While restricting vehicle dwell time is widely used to manage commercial vehicle parking behavior, there is insufficient data to help assess the effectiveness of these restrictions, which makes it difficult for policymakers to account for the complexity of commercial vehicle parking behavior.

This is accomplished by using generalized linear models with data collected from five buildings that are known to include commercial vehicle activities in the downtown area of Seattle, Washington, USA. Our models showed that dwell times for buildings with concierge services tended to be shorter. Deliveries of documents also tended to have shorter dwell times than oversized supplies deliveries. Passenger vehicle deliveries had shorter dwell times than deliveries made with vehicles with roll-up doors or swing doors (e.g., vans and trucks). When there were deliveries made to multiple locations within a building, the dwell times were significantly longer than dwell times made to one location in a building. The findings from the presented models demonstrate the potential for improving future parking policies for commercial vehicles by considering data collected from different building types, delivered goods, and vehicle types.

Authors: Haena KimDr. Anne Goodchild, Linda Ng Boyle

Recommended Citation:
Kim, H., Goodchild, A., & Boyle, L. N. (2021). Empirical analysis of commercial vehicle dwell times around freight-attracting urban buildings in downtown Seattle. Transportation Research Part A: Policy and Practice, 147, 320–338. https://doi.org/10.1016/j.tra.2021.02.019

Technical Report

The Final 50 Feet of the Urban Goods Delivery System: Pilot Test of an Innovative Improvement Strategy

Download PDF (3.07 MB)

URL: http://hdl.handle.net/1773/44473

Publication: Pacific Northwest Transportation Consortium (PacTrans)

Publication Date: 2019

Summary:

This report presents a pilot test of a common carrier smart locker system — a promising strategy to reduce truck trip and failed first delivery attempts in urban buildings. The Urban Freight Lab tested this system in the 62-story Seattle Municipal Tower skyscraper in downtown Seattle.

The Urban Freight Lab identified two promising strategies for the pilot test: (1) Locker system: smaller- to medium-sized deliveries can be placed into a locker that was temporarily installed during the pilot test; and (2) Grouped-tenant-floor-drop-off-points for medium-sized items if the locker was too small or full (4-6 floor groups set up by Seattle Department of Transportation and Seattle City Light).

Users picked up their goods at the designated drop-off points. Flyers with information on drop-off-points were given to the carriers. UFL researchers evaluated the ability of the standardized second step pilot test to reduce the number of failed first delivery attempts by (1) Collecting original data to document the number of failed first delivery attempts before and after the pilot test; and (2) Comparing them to the pilot test goals.

Authors: Haena KimBarbara IvanovDr. Anne Goodchild

Recommended Citation:
Goodchild, A., Kim, H., & Ivanov, B. Final 50 Feet of the Urban Goods Delivery System: Pilot Test of an Innovative Improvement Strategy. (2019)

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.