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.

Paper

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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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

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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

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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

A Holistic Data-Driven Framework for Curb Space Use and Policy-Making

The curb space is the portion of the public rights-of-way that demarcates the roadway from the sidewalk, separating pedestrian flow from moving vehicles. It is a scarce public resource that has been traditionally used for storing private passenger vehicles. However, the past decade has seen not only a surge in demand but also the rise of new demands for curb space, driven by new forces of change: the rise in online shopping has driven up the demand for delivery vehicle loading and unloading spaces; the increasing use of ride-hailing vehicles such as Uber and Lyft has exacerbated curb space congestion; the rapid adoption of micromobility modes has increased their parking demand, among others. The pandemic has only exacerbated the issue due to greater demand for home delivery services and novel use cases such as curbside cafes.

The mismatch between the increase in demand and the lack of curb space supply represents a bottleneck in the urban transportation system, increasing the cruising for parking time — the time drivers spend searching for parking — as well as the occurrence of unauthorized parking. Both consequences heavily impact urban traffic congestion, increasing emissions and lowering the quality of life for urban dwellers, as well as can potentially create unsafe conditions. More broadly, the curb is a major linchpin in city operations: beyond congestion, it also affects business district vitality, residential access, and even policy decisions about new constructions.

To address these challenges, cities need greater access to data science and machine learning tools to have better insights into the overall use of and demand for curb space, with the final objective to be able to effectively manage the limited amount of curb space available. This includes the need for tools to aid in optimizing pricing mechanisms and to adaptively learn the most efficient and sustainable allocation of space to the different types of users.

Two research groups at the University of Washington have taken different but complementary approaches to study the curb and build tools to help cities understand different curb demands and better manage the limited curb space available.

The Urban Freight Lab, led by Prof. Anne Goodchild, approaches the study of the curb from the perspective of commercial vehicles, including delivery and ridehailing vehicles. The group has collected data and derived statistical models of curb users’ behaviors for commercial vehicles. Furthermore, the group has piloted on-the-ground technologies and policies to improve curb access. In a recent project, Prof. Goodchild’s group deployed 300 in-ground occupancy sensors at commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) — curb spaces dedicated to commercial and ridehailing vehicles — in a 10-block study area in the Belltown neighborhood of Seattle, WA, collecting more than a year of fine-grained curb-use data.

The research group led by Prof. Lilian Ratliff approaches the study of the curb primarily from the perspective of private passenger vehicles, applying innovative machine learning and game theory tools to study curb management policies. In a recent project, Prof. Ratliff’s group developed a new modeling framework to estimate on-street paid parking occupancies — spaces dedicated for private passenger vehicle parking — from parking transaction data and sparse ground truth occupancy data obtained via manual counts and timelapse camera images.

The research in Goodchild’s and Ratliff’s groups has been impactful. Yet, load zone and paid parking curb-uses are highly interdependent given that the zones dedicated to the different use cases are often on the same curb. Hence, a more holistic approach to learning curb use behaviors is needed in order to effectively manage the whole curb.

For this project, the two groups will collaborate to integrate different data streams currently being collected separately and in an uncoordinated way, including data from in-ground curb sensors at CVLZs and PLZs, paid parking transactions at paid parking spaces, and data obtained from timelapse camera recordings. With such a complete dataset, the groups will create a holistic framework to analyze not only the curb behaviors of different users but also how different users interact in the competition for limited curb space.

The proposed collaboration will advance the state of the art in environmental sciences by providing the most complete dataset and creating innovative tools to inform policymaking on curb parking pricing and curb allocation to reduce cruising for parking and unauthorized parking events, therefore tackling the climate crisis by reducing urban vehicle emissions and traffic congestion.

The proposed collaboration will also advance the state of the art in data science by developing a new statistical framework and machine learning algorithms to analyze curb space use behaviors from different curb space users and develop much-needed recommendations for cities on how to better allocate curb space to different competing demands.

The project will have a direct impact on the City of Seattle as both groups are currently collaborating with the Seattle Department of Transportation to create a more data-driven decision-making framework for curb space policies, as well as an impact on the fields of urban transportation and logistics by merging two separate kinds of literature, the more traditional transport theory taking private passenger vehicles as the main actor in urban transportation and the urban logistics field that focuses on commercial vehicles operations in urban areas.

Concrete outcomes of the projects obtained during the year of collaboration will include a joint seminar series of the two groups, presenting their ongoing research projects that focused on the curb, a join effort to collect data in Seattle, and integrating data streams to generate a complete dataset of curb use for the Seattle downtown area. Additionally, the groups will jointly write a scientific paper proposing a holistic framework to analyze the curb from the different users’ perspectives. The proposed collaboration will expand upon the projects Prof. Goodchild’s and Prof. Ratliff’s groups are currently working on, and develop a new set of data and tools that will enable future joint grant proposals by the two groups.

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

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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

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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

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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)

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

Presentation

Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities: The Urban Freight Lab’s Innovative Solutions

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URL: https://catc.ca.gov/-/media/ctc-media/documents/tab-10-3-a11y.pdf

Publication: California Transportation Commission (August 15, 2018)

Publication Date: 2018

Summary:

A 20% e-commerce compound annual growth rate (CAGR) would more than double goods deliveries in 5 years. If nothing changes, this could double delivery trips in cities; thereby doubling the demand for load/unload spaces.

Innovation is needed to manage scarce curbs, alleys, and private loading bay space in the new world of on-demand transportation, 1-hour e-commerce deliveries, and coming autonomous vehicle technologies.

The Urban Freight Lab at the University of Washington (UW), in partnership with the City of Seattle Department of Transportation (SDOT), is using a systems engineering approach to solve delivery problems that overlap cities’ and businesses’ spheres of control.

The Urban Freight Lab is a living laboratory where potential solutions are generated, evaluated, and pilot-tested inside urban towers and on city streets.

Authors: Dr. Anne Goodchild

Recommended Citation:
Goodchild, Anne. Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities: The Urban Freight Lab’s Innovative Solutions. California Transportation Commission (August 15, 2018)