Skip to content
Urban Freight Lab

Research Keyword: Goods movement

Goods movement is the transportation of, and terminal activities associated with, the movement of goods.

Blog

The art of (mis)loading deliveries

URL: https://www.goodsmovement2030.com/post/the-art-of-mis-loading-deliveries
Publication: Goods Movement 2030, an Urban Freight Blog
Publication Date: 2024
Summary:

Imagine the frustration of searching for a misplaced item, like your house keys or wallet, before leaving for a night out. Now, picture a FedEx or Amazon delivery driver halfway through a tight morning route, struggling to locate a parcel due by 9 a.m. while parked right outside the customer’s address.

These misloads — where shipments are accidentally loaded onto the wrong delivery route or vehicle — not only cause stress and lost time for the delivery driver but also result in significant negative economic and environmental impacts. Misloads can also lead to customer dissatisfaction, erode trust in the delivery company, and necessitate additional vehicle travel miles to rectify the mistake. Despite this, little is known about the frequency of human errors in last-mile delivery and how they affect the overall supply chain. In this post, we define the concept of misloading and unpack some of these questions to better understand its implications and identify potential solutions.

What is misloading?

Misloading is generally considered an error in the Load Planning Problem (LPP). An LPP is a discrete optimization problem that considers a logistic network structure (set of nodes, or logistics terminals, and links, routes connecting terminals served by a given fleet of trucks) and the demand for freight (quantity, origin, and destination). The objective is to determine the optimal sequence of terminals that a load of freight should traverse to minimize handling costs and maintain a specified level of service. The outcome of an LPP is a “load plan,” which details a unique strategy to handle each shipment at every point in the system (Powell & Sheffi, 1983).

A shipment misload is a deviation from the load plan, which could occur due to intentional or unintentional actions. For example, during a ridealong I performed on a parcel delivery route in downtown Seattle (Dalla Chiara et al., 2020), the driver chose to deliver a bulky carpet earlier in the morning instead of the afternoon ahead of schedule in the morning rather than the afternoon, in order to create space inside the vehicle to safely and efficiently move around and retrieve packages from the shelves. Such intended deviation from the load plan improved the efficiency of the overall route. Conversely, unintended misloads often occur due to human errors (a shipment is misplaced on the wrong vehicle or route) or machine errors (a shipment is incorrectly labeled).

Based on the stage in the supply chain where they occur, misloads can also be classified as hub-to-hub or preload misload. Hub-to-hub misloading occurs when the mis-shipment is during a package transfer between two depots (for example, a package mistakenly sent to Vancouver, B.C., Canada, instead of Vancouver, WA, USA). Preload misloading happens at the last-mile facility — the last leg of a supply chain, where shipments are scanned, sorted, and loaded into delivery vehicles either by a driver or a preloader. At this stage, the a shipment may be placed on the wrong route, either due to human or upstream label errors.

Frequency of misloaded packages

Misloading is often reported as a misloading rate (or its corresponding order accuracy rate) calculated by dividing the number of misloads by the total number of deliveries during a given time period.

The misload rate varies across industry sector, leg of the supply chain (whether hub-to-hub or preload), and even geographical location of logistics facilities. In the fast-moving goods sector, hub-to-hub misloads rate are reported to range from 0.01% to 0.1%, while preload misload rates have been reported between 0.1% and 0.3%.

While this may seem relatively small, misloading occurs daily due to the vast scale of delivery operations. For example, with a 0.2% misload rate, approximately one in 500 parcels is misloaded. Considering that a typical parcel delivery van handles around 250 packages per route, on average, every two vehicles would contain one misloaded package. Even with a lower misload rate of 0.1% (one in 1,000 packages), there would still be one misloaded package for every four delivery vehicles. In Seattle, where approximately 900 parcel delivery vehicles enter the greater downtown area daily (Giron-Valderrama & Goodchild, 2020), this equates to more than 200 misloaded packages every day. These figures highlight the frequency of misloading incidents despite their seemingly low percentage, and underscore the impact on operational efficiency and customer service.

We note that the misload rate increases the closer we get to the last mile of a delivery journey in the fast-moving consumer goods sector. From the data above, the misload rate quadrupled from the hub-to-hub to the last-mile segment (from 0.05% to 0.2%). This reflects increased manual labor, reduced automation, and increased complexity in handling smaller, non-standard parcels.

Quantifying the impact of misloading

Quantifying the economic and environmental loss of a misloaded package involves first understanding how drivers respond to these errors.

 

A preload misload is typically identified when a driver has either a missing package they are supposed to deliver or an additional package that does not belong on their assigned route. What happens next will depend on procedures implemented by the facility and other operational factors. In the case of a missing package deemed “critical,” the driver would typically alert nearby routes where the misloaded package is likely to have been placed). The driver might meet the other driver halfway, or the other driver may make the additional delivery. A “non-critical” package may be returned to the facility and rescheduled for delivery the following day. In either case, misloads result in additional miles traveled and the loss of driver time.

Quantifying the negative impacts of misloading is a difficult task. Transportation science often uses simulation tools to test different scenarios that are difficult to measure empirically by generating mathematical models. In this case, a misloading simulator takes as input the existing delivery demand and misload rate, calculates the optimal load plan, and outputs the total vehicle miles traveled (VMT) and total route time under scenarios both with and without misloads. By running simulations with varying parameters (different demands and misload rates), the misload simulator can provide a sufficiently precise estimate of how the misloads affects route performance.

According to the previous section, misloading can cause three possible scenarios, depicted in the figure below. In all three scenarios, we identified two routes — the red route carrying the misloaded shipment, the blue route missing the misloaded shipment — and the full node representing the final destination of the misloaded shipment.

  • Scenario A simulates the case of a misloaded non-critical package; in this scenario, the impact of misload is the additional VMT and time the driver spends on the blue route to reach the customer without being able to complete the delivery, as the shipment was misloaded on the vehicle carrying out the red route.
  • Scenario B simulates the case of a misloaded critical package, where the driver of the red route is required to spend extra time and VMT to make an additional delivery.
  • Scenario C simulates the case of a misloaded critical package, in which the driver of the blue route needs to spend additional time and VMT to meet the driver on the red route and retrieve the misloaded package.

The shape and length of delivery routes are extremely heterogeneous and vary among carriers, business sectors, and contexts. For instance, if we consider the case of a typical parcel delivery carrier delivering in downtown Seattle, a route averages 7.2 miles, with 24 stops, and an average distance of 0.3 miles per stop. A beverage company delivering in downtown Seattle typically has a 15-mile route with 11 stops and an average of 1.4 miles per stop (Dalla Chiara et al., 2021). Considering the simplest scenario to simulate (scenario A) and assuming the above-discussed misload rate of one misloaded shipment every two routes, a single misload would result in an additional 0.6 miles of travel, representing 4% of the total VMT. In the case of the beverage distributor, a single misload would leads to an additional 2.8 miles traveled, constituting 9% of total VMT.

Addressing misloading

Despite their statistical infrequency, misloads occur daily, affecting delivery times, increasing VMT, and eroding customer trust. Delivery companies strive to meet and exceed their misload target rates, but often struggle to identify effective solutions.

Addressing misloads involves a multifaceted approach that combines improved training and the adoption of advanced technologies. Developing clear procedures and providing training for drivers and preloaders can reduce human errors in labeling, sorting, scanning, and loading, as well as in detecting and correcting misloads. The Service Awareness Label Training (SALT) practice helps improve error detection. SALT involves placing fake misloaded packages in the system to assess employees’ ability to identify them.

Recent advancements in tracking technologies are creating new opportunities for delivery companies to reduce misloading. Since the introduction of scanning (the first item marked with a Universal Product Code was scanned in 1974 in a supermarket in Troy, OH, Weightman, 2015), most parcels are now scanned at key checkpoints, reducing human errors, generating a wealth of data that can be used to optimize the supply chain, and providing customers with real-time location and status information about their parcels.

Radio-frequency identification (RFID) technology, which allows multiple simultaneous scans, has allowed for substantial efficiency gains throughout the supply chain (Fan et al., 2015), enabling seamless tracking and reducing manual effort. While cost has historically been a major obstacle to full deployment (Bottani and Rizzi, 2008), 2022 seemed to be a tipping point in RFID implementation at scale (Swedberg, 2022). For instance, UPS launched a smart package initiative starting in 2022, deploying an RFID-based system through its facilities (Garland, 2022). The system involves placing RFID scanners on wearable devices and on delivery vehicle rear doors to automate preloading and eliminate manual scanning — and, therefore, the likelihood of misloads. Also beginning in September 2022, global retailer Walmart mandated that suppliers across several departments include RFID tags on all products shipped to its warehouses.

What’s next?

While the impact of misloading has been viewed mostly from a customer service perspective, its broader economic and environmental impacts are often overlooked. Implementing technologies like RFID can reduce misload rates, yet companies must weigh the cost and benefits of such investments. Quantifying the benefits of reducing misloads, such as decreasing VMT, lowering vehicle emissions, and improving drivers’ efficiency (among other potential efficiencies, for instance, Brewster, 2024) is important to guide companies in making informed decisions and optimize strategies.

Acknowledgements

The author would like to acknowledge IMPINJ for their technical and financial support and the experts and practitioners who provided content for this article.

References

Authors: Dr. Giacomo Dalla Chiara
Report

The Final 50 Feet of the Urban Goods Delivery System (Final Report)

 
Download PDF  (6.73 MB)
Publication Date: 2018
Summary:

Urban Freight Lab’s foundational report is the first assessment in any American city of the privately-owned and operated elements of the Final 50 Feet of goods delivery supply chains (the end of the supply chain, where delivery drivers must locate both parking and end customers). These include curb parking spaces, private truck freight bays and loading docks, street design, traffic control, and delivery policies and operations within buildings.

Goods delivery is an essential but little-noticed activity in urban areas. For the last 40 years, deliveries have been mostly performed by a private sector shipping industry that operates within general city traffic conditions. However, in recent years e-commerce has created a rapid increase in deliveries, which implies an explosion of activity in the future.

Meeting current and future demand is creating unprecedented challenges for shippers to meet both increased volumes and increasing customer expectations for efficient and timely delivery. Anecdotal evidence suggests that increasing demand is overwhelming goods delivery infrastructure and operations. Delivery vehicles parked in travel lanes, unloading taking place on crowded sidewalks, and commercial truck noise during late night and early morning hours are familiar stories in urban areas.

These conditions are noticeable throughout the City of Seattle as our population and employment rapidly increase. However, goods delivery issues are particularly problematic in Seattle’s high-density areas of Downtown, Belltown, South Lake Union, Pioneer Square, First Hill, Capitol Hill and Queen Anne, described as Seattle’s “Center City”. Urban goods transportation makes our economy and quality of life possible.

As the Seattle Department of Transportation (SDOT) responds to the many travel challenges of a complex urban environment, we recognize that goods delivery needs to be better understood and supported to retain the vitality and livability of our busiest neighborhoods.

U.S. cities do not have much information about the urban goods delivery system. While public agencies have data on city streets, public transportation and designated curbside parking, the “final 50 feet” in goods delivery also utilizes private vehicles, private loading facilities, and privately-owned and operated buildings outside the traditional realm of urban planning.

Bridging the information gap between the public and private sectors requires a new way of thinking about urban systems. Specifically, it requires trusted data sharing between public and private partners, and a data-driven approach to asking and answering the right questions, to successfully meet modern urban goods delivery needs.

The Urban Freight Lab (UFL) provides a standing forum to solve a range of short-term as well as long-term strategic urban goods problem solving, that provides evidence of effectiveness before strategies are widely implemented in the City.

Authors: Urban Freight Lab
Recommended Citation:
Supply Chain Transportation & Logistics Center. (2018) The Final 50 Feet of the Urban Goods Delivery System.
White Paper

Biking the Goods: How North American Cities Can Prepare for and Promote Large-Scale Adoption of E-Cargo Bikes

 
Download PDF  (1.79 MB)
URL: https://doi.org/10.6069/REXD-W642
Publication Date: 2023
Summary:

The distribution of goods and services in North American cities has conventionally relied on diesel-powered internal combustion engine (ICE) vehicles. Recent developments in electromobility have provided an opportunity to reduce some of the negative externalities generated by urban logistics systems.

Cargo e-bikes — electric cycles specially designed for cargo transportation — represent an alternative environmentally friendly and safer mode for delivering goods and services in urban areas. However, lack of infrastructure, legal uncertainties, and a cultural and economic attachment to motorized vehicles has hindered their adoption. Cities play a crucial role in reducing these barriers and creating a leveled playing field where cargo e-bikes can be essential to urban logistics systems.

This paper aims to inform urban planners about what cargo e-bikes are, how they have been successfully deployed in North America to replace ICE vehicles, and identify actionable strategies cities can take to encourage their adoption while guaranteeing safety for all road users.

Gathering data and opinions from key public and private sector stakeholders and building on the expertise of the Urban Freight Lab, this paper identifies nine recommendations and 21 actions for urban planners across the following four main thematic areas:

  1. Infrastructure: cycling, parking infrastructure, and urban logistics hubs
  2. Policy and Regulation: e-bike law, safety regulation, and policies de-prioritizing vehicles
  3. Incentives: rebates and business subsidies
  4. Culture and Education: labor force training, educational programs, and community-driven adoption

Acknowledgements

The Urban Freight Lab acknowledges the following co-sponsors for financially supporting this research: Bosch eBike Systems, Fleet Cycles, Gazelle USA, Michelin North America, Inc., Net Zero Logistics, Pacific Northwest Transportation Consortium (PacTrans) Region 10, Seattle Department of Transportation, and Urban Arrow.

Technical contributions and guidance: Amazon, B-Line (Franklin Jones), Cascade Bicycle Club, Coaster Cycles, City of Boston, City of Portland, Downtown Seattle Business Association (Steve Walls), New York City Department of Transportation, People for Bikes (Ash Lovell), Portland Bureau of Transportation, University of Washington Mailing Services (Douglas Stevens), UPS,

Authors: Dr. Giacomo Dalla ChiaraRishi VermaKelly RulaDr. Anne Goodchild
Recommended Citation:
Dalla Chiara, G., Verma, R., Rula, K., Goodchild, A. (2023). Biking the Goods: How North American Cities Can Prepare for and Promote Large-Scale Adoption of Cargo e-Bikes. Urban Freight Lab, University of Washington.
Report

Final Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

 
Download PDF  (7.07 MB)
Publication Date: 2022
Summary:

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

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

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

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

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

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

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

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

Key findings from the technology and strategies employed:

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

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

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

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

Common carrier lockers create delivery density, helping delivery people complete their work faster. The driver parks next to the locker system, loads packages into it, and returns to the truck. When delivery people spend less time going door-to-door (or floor-to-floor inside a building), it cuts the time their truck needs to be parked, increasing turnover and adding parking capacity in crowded cities. This project piloted and collected data on common carrier lockers in three study area buildings.

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

  • The implementation of the parcel locker allowed delivery drivers to increase productivity: 40%-60% reduction in time spent in the building and 33% reduction in vehicle dwell time at the curb.
Authors: Dr. Anne GoodchildDr. Giacomo Dalla ChiaraFiete KruteinDr. Andisheh RanjbariDr. Ed McCormackElizabeth Guzy, Dr. Vinay Amatya (PNNL), Ms. Amelia Bleeker (PNNL), Dr. Milan Jain (PNNL)
Recommended Citation:
Urban Freight Lab (2022). Final Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.
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

Delivery Process for an Office Building in the Seattle Central Business District

 
Download PDF  (1.43 MB)
URL: https://trid.trb.org/view/1497161
Publication: Transportation Research Record: Journal of the Transportation Research Board
Volume: Transportation Research Board 97th Annual Meeting
Publication Date: 2018
Summary:

Movement of goods within a central business district (CBD) can be very constraining with high levels of congestion and insufficient curb spaces. Pick-up and delivery activities encompass a significant portion of urban goods movement and inefficient operations can negatively impact the already highly congested areas and truck dwell times. Identifying and quantifying the delivery processes within the building is often difficult.

This paper introduces a systematic approach to examine freight movement, using a process flow map with quantitative delivery times measured during the final segment of the delivery process. This paper focuses on vertical movements such as unloading/loading activities, taking freight elevators, and performing pick-up/delivery operations. This approach allows us to visualize the components of the delivery process and identify the processes that consume the most time and greatest variability. Using this method, the authors observed the delivery process flows of an office building in downtown Seattle, grouped into three major steps: 1. Entering, 2. Delivering, 3. Exiting. This visualization tool provides researchers and planners with a better understanding of the current practices in the urban freight system and helps identify the non-value-added activities and time that can unnecessarily increase the overall delivery time.

Authors: Haena KimDr. Anne Goodchild, Linda Ng Boyle
Recommended Citation:
Kim, Haena, Linda Ng Boyle, and Anne Goodchild. "Delivery Process for an Office Building in the Seattle Central Business District." Transportation Research Record 2672, no. 9 (2018): 173-183. 
Report

The Final 50 Feet of the Urban Goods Delivery System (Executive Summary)

 
Download PDF  (1.91 MB)
Publication Date: 2018
Summary:

Urban Freight Lab’s foundational report is the first assessment in any American city of the privately-owned and operated elements of the Final 50 Feet of goods delivery supply chains (the end of the supply chain, where delivery drivers must locate both parking and end customers). These include curb parking spaces, private truck freight bays and loading docks, street design, traffic control, and delivery policies and operations within buildings.

Two key goals have been identified early for the Final 50 Feet program:

  • Reducing truck time in a load/unload space in the city (“dwell time”)
  • Minimizing failed first package deliveries. About 8-10% of first delivery attempts in urban areas are unsuccessful, creating more return trips
Authors: Urban Freight Lab
Recommended Citation:
Supply Chain Transportation & Logistics Center. (2018) The Final 50 Feet of the Urban Goods Delivery System: Executive Summary.
Paper

Evaluating the Impacts of Density on Urban Goods Movement Externalities

URL: https://doi.org/10.1080/17549175.2017.1310745
Publication: Journal of Urbanism: International Research on Placemaking and Urban Sustainability
Volume: 10:04
Pages: 13-Jan
Publication Date: 2017
Summary:

Research has established a potential to reduce vehicle miles traveled (VMT) by replacing passenger travel for shopping with delivery service, and a few studies have indicated CO2 emissions can also be reduced. However, that research has mostly focused on urban locations and has not addressed criteria pollutants. This study examines the impacts of replacing passenger travel for shopping with delivery service over a broader set of externalities (VMT, CO2, NOx, and PM10) in both urban and rural communities. Three different goods movement strategies are considered in three different municipalities in King County, Washington, which vary in size, density, and distance from the metropolitan core. The research finds that delivery services can reduce VMT over passenger vehicle travel for shopping, however, the potential to reduce CO2, NOx, and PM10 emissions varies by municipality. Significant trade-offs are observed between VMT and emissions – especially between VMT and criteria pollutants.

Authors: Dr. Anne Goodchild, Erica Wygonik
Recommended Citation:
Wygonik, Erica, and Anne Goodchild. Evaluating the Impacts of Density on Urban Goods Movement Externalities. Journal of Urbanism: International Research on Placemaking and Urban Sustainability 10, no. 4 (2017): 487-499. 
Thesis: Array
Chapter

Comparison of Vehicle Miles Traveled and Pollution from Three Goods Movement Strategies

URL: http://dx.doi.org/10.1108/S2044-994120140000006003
Publication: Sustainable Logistics: Transport and Sustainability (Emerald Group Publishing Limited)
Volume: Volume 6
Pages: 63-82
Publication Date: 2014
Summary:

This chapter provides additional insight into the role of warehouse location in achieving sustainability targets and provides a novel comparison between delivery and personal travel for criteria pollutants.

Purpose: To provide insight into the role and design of delivery services to address CO2, NO x , and PM10 emissions from passenger travel.Methodology/approach: A simulated North American data sample is served with three transportation structures: last-mile personal vehicles, local-depot-based truck delivery, and regional warehouse-based truck delivery. CO2, NO x , and PM10 emissions are modeled using values from the US EPA’s MOVES model and are added to an ArcGIS optimization scheme.Findings: Local-depot-based truck delivery requires the lowest amount of vehicle miles traveled (VMT), and last-mile passenger travel generates the lowest levels of CO2, NO x , and PM10. While last-mile passenger travel requires the highest amount of VMT, the efficiency gains of the delivery services are not large enough to offset the higher pollution rate of the delivery vehicle as compared to personal vehicles.

Practical implications: This research illustrates the clear role delivery structure and logistics have in impacting the CO2, NO x , and PM10 emissions of goods transportation in North America.

Social implications: This research illustrates the tension between goals to reduce congestion (via VMT reduction) and CO2, NO x , and PM10 emissions.

Originality/value: This chapter provides additional insight into the role of warehouse location in achieving sustainability targets and provides a novel comparison between delivery and personal travel for criteria pollutants.

Authors: Dr. Anne Goodchild, Erica Wygonik
Recommended Citation:
Wygonik, Erica, and Anne Goodchild. "Comparison of vehicle miles traveled and pollution from three goods movement strategies." Sustainable Logistics, pp. 63-82. Emerald Group Publishing Limited, 2014.