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

Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China

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URL: https://doi.org/10.3390/su12239918

Publication: Sustainability

Volume: 12(23)

Publication Date: 2020

Summary:

This paper discusses how to promote high-speed rail (HSR) freight business by solving the congestion problem. First, we define the existing operation modes in China and propose the idea of relieving congestion by reserving more carriages of HSR passenger trains for freight between cities with large potential volume or small capacity. Second, we take one HSR corridor as a case to study, and use predictive regression and integrated time series methods to forecast the growth of HSR freight volume along the corridor. Finally, combined with forecast results and available capacity during the peak month of 2018, we offer suggestions on the mode adoption in each segment during the peak month from 2019 to 2022. Results demonstrate: (1) Among all 84 Origin-Destination (OD) city flows, the percentage of those monthly volumes over 1 ton increases from 17.9% in 2018 to 84.6% in 2022, and those over 30 tons rise from 3.6% to 26.2%. (2) Among the segments between seven main cities in the HSR corridor, T-J should be given priority to operate trains with reserved mode; the segment between X and J deserves to reserve most carriages during the peak month in the future. Specifically, our model suggests reserving 5.3–10.1 carriages/day for J-X, and 4.8–16.3 carriages/day for X-J during the peak month from 2019 to 2022.

Authors: Hanlin GaoDr. Anne Goodchild, Meiqing Zhang

Recommended Citation:
Hanlin Gao, Meiqing Zhang, & Anne Goodchild. (2020). Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China. Sustainability (Basel, Switzerland), 12(23). https://doi.org/10.3390/su12239918

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 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.

Report

Cargo E-Bike Delivery Pilot Test in Seattle

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Publication Date: 2020

Summary:

This study performed an empirical analysis to evaluate the implementation of a cargo e-bike delivery system pilot tested by the United Parcel Service, Inc. (UPS) in Seattle, Washington. During the pilot, a cargo e-bike with a removable cargo container was used to perform last-mile deliveries in downtown Seattle. Cargo containers were pre-loaded daily at the UPS Seattle depot and loaded onto a trailer, which was then carried to a parking lot in downtown.

Data were obtained for two study phases. In the “before-pilot” phase, data were obtained from truck routes that operated in the same areas where the cargo e-bike was proposed to operate. In the “pilot” phase, data were obtained from the cargo e-bike route and from the truck routes that simultaneously delivered in the same neighborhoods. Data were subsequently analyzed to assess the performance of the cargo e-bike system versus the traditional truck-only delivery system.

The study first analyzed data from the before-pilot phase to characterize truck delivery activity. Analysis focused on three metrics: time spent cruising for parking, delivery distance, and dwell time. The following findings were reported:

On average, a truck driver spent about 2 minutes cruising for parking for each delivery trip, which represented 28 percent of total trip time. On average, a driver spent about 50 minutes a day cruising for parking.
Most of the deliveries performed were about 30 meters (98 feet) from the vehicle stop location, which is less than the length of an average blockface in downtown Seattle (100 meters, 328 feet). Only 10 percent of deliveries were more 100 meters away from the vehicle stop location.
Most truck dwell times were around 5 minutes. However, the dwell time distribution was right-skewed, with a median dwell time of 17.5 minutes.

Three other metrics were evaluated for both the before-pilot and the pilot study phases: delivery area, number of delivery locations, and number of packages delivered and failed first delivery rate. The following results were obtained:

A comparison of the delivery areas of the trucks and the cargo e-bike before and after the pilot showed that the trucks and cargo e-bike delivered approximately in the same geographic areas, with no significant changes in the trucks’ delivery areas before and during the pilot.
The number of establishments the cargo e-bike delivered to in a single tour during the pilot phase was found to be 31 percent of the number of delivery locations visited, on average, by a truck in a single tour during the before-pilot phase, and 28 percent during the pilot phase.
During the pilot, the cargo e-bike delivered on average to five establishments per hour, representing 30 percent of the establishments visited per hour by a truck in the before-pilot phase and 25 percent during the pilot.
During the pilot, the number of establishments the cargo e-bike delivered to increased over time, suggesting a potential for improvement in the efficiency of the cargo e-bike.
The cargo e-bike delivered 24 percent of the number of packages delivered by a truck during a single tour, on average, before the pilot and 20 percent during the pilot.
Both before and during the pilot the delivery failed rate (percentage of packages that were not delivered throughout the day) was approximately 0.8 percent. The cargo e-bike experienced a statistically significantly lower failed rate of 0.5 percent with respect to the truck fail rate, with most tours experiencing no failed first deliveries.

The above reported empirical results should be interpreted only in the light of the data obtained. Moreover, some of the results are affected by the fact that the pilot coincided with the holiday season, in which above average demand was experienced. Moreover, because the pilot lasted only one month, not enough time was given for the system to run at “full-speed.”

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). Cargo E-Bike Delivery Pilot Test in Seattle.

Article, Special Issue

Urban Logistics: From Research to Implementation

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URL: https://doi.org/10.1016/j.rtbm.2022.100913

Publication: Research in Transportation Business & Management (RTBM)

Volume: 45 (A)

Publication Date: 2022

Summary:

To address the accessibility and sustainability challenges of urban logistics it is important to consider urban logistics from a number of perspectives.

This includes considering:

spatial context i.e. not focusing solely on the urban center or core but also in terms of actions taken in broader logistics and supply chain management.
stakeholders i.e. including all key decision makers and constituents.
complexity and heterogeneity of activities (range of vehicles used, the products carried, location of distribution centers, and the variety found in city size, form, and governance).

This diversity of perspectives, and their influence on the urban freight system, makes it challenging to identify simple solutions to problems.

A number of forces are also at work impacting change in the urban logistics system. Technological innovation affecting urban logistics includes digitalization, e.g. the Internet of Things (important in terms of connected objects) and big data. These developments are already established and beginning to have an impact or at least implications in the field of urban logistics and freight transport. However, problems will not be solved by technology alone and it is essential to understand how behavior (at the individual and corporate level) influences outcomes and needs to change. Research needs to address interactions between stakeholders and the role of city authorities in promoting innovation and change.

Cities are complex environments and urban logistics has to adapt to these demands. The complexity of cities also gives rise to a debate about the extent to which problems (and their possible solutions) may be considered context-specific. This leads to questions relating to how initiatives should be scaled up to gain greater traction in dealing with challenges now and in the future. It is important to learn as much as possible from the high number of projects and new services that have been implemented in cities over the past ten years. These range from initiatives related to electric vehicles, through locker box systems and the role of the receiver in making change happen. How to learn and then apply the lessons from projects is an important question. In many cases it has been argued that the underlying business model has not been addressed successfully leading to the problem of projects lasting only as long as some form of project funding is available.

Authors: Dr. Anne Goodchild, Michael Browne (University of Gothenburg)

Recommended Citation:
Michael Browne, Anne Goodchild. Urban Logistics: From Research to Implementation, Research in Transportation Business & Management, Volume 45 (A) 2022, 100913, ISSN 2210-5395, https://doi.org/10.1016/j.rtbm.2022.100913.

Technical Report

Common MicroHub Research Project: Research Scan

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Publication Date: 2020

Summary:

This research scan revealed a lack of an established and widely accepted definition for the concept of consolidation centers or microhubs. Many recent implementations of urban freight consolidation have focused on bundling goods close to the delivery point by creating logistical platforms in the heart of urban areas. These have shared a key purpose: to avoid freight vehicles traveling into urban centers with partial loads.

To establish definitions of micro-consolidation and its typologies, it is important to review previous efforts in the literature that have explained and evaluated urban consolidation centers and lessons that have led to the search for new alternatives. Starting in 1970s, the urban consolidation center (UCC) concept was implemented in several European cities and urban regions. These were mostly led by commercial enterprises with temporary or even structural support from the government to compensate for additional transshipment costs. Allen et. al. defined the UCC as a “logistic base located in the vicinity of the place of performing services (e.g., city centers, whole cities, or specific locations like shopping malls) where numerous enterprisers deliver goods destined for the serviced area from which consolidated deliveries as well as additional logistic and retailed services are realized”.

Many of these implementations failed to operate in the long term because of low throughput volumes, the inability to operate without financial support from government, and dissatisfaction with service levels. The cost of having an additional transshipment point often prevented the facilities from being cost-effective, and they could not operate when governmental subsidies were removed (4). From a commercial perspective, experiences with publicly operated UCCs were mostly negative, and centers that have operated since 2000 are often run single-handedly by major logistics operators.

Although it appears that many UCCs were not successful, that does not mean that the idea of an additional transshipment point should be sidelined completely (4). Several studies have mentioned the micro-consolidation concept as a transition from the classic UCC. Learning from previous experiences, Janjevic et. al. defined micro-consolidation centers as facilities that are located closer to the delivery area and have a more limited spatial range for delivery than classic UCCs. Similarly, Verlinde et. al., referred to micro-consolidation centers as “alternative” additional transshipment points that downscale the scope of the consolidation initiative further than a UCC.

In this project, a delivery microhub (or simply a microhub) was defined as a special case of UCC with closer proximity to the delivery point and serving a smaller range of service area. A microhub is a logistics facility where goods are bundled inside the urban area boundaries, that serves a limited spatial range, and that allows a mode shift to low-emission vehicles or soft transportation modes (e.g., walking or cargo bikes) for last-mile deliveries.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). Common MicroHub Research Project: Research Scan.

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

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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.

Technical Report

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

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Publication: U.S. Department of Energy

Publication Date: 2019

Summary:

The objectives of this project are to develop and implement a technology solution to support research, development, and demonstration of data processing techniques, models, simulations, a smart phone application, and a visual-confirmation system to:

Reduce delivery vehicle parking seeking behavior by approximately 20% in the pilot test area, by returning current and predicted load/unload space occupancy information to users on a web-based and/or mobile platform, to inform real-time parking decisions
Reduce parcel truck dwell time in pilot test areas in Seattle and Bellevue, Washington, by approximately 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems, and
Improve the transportation network (which includes roads, intersections, warehouses, fulfillment centers, etc.) and commercial firms’ efficiency by increasing curb occupancy rates to roughly 80%, and alley space occupancy rates from 46% to 60% during peak hours, and increasing private loading bay occupancy rates in the afternoon peak times, in the pilot test area.

The project team has designed a 3-year plan, as follows, to achieve the objectives of this project.

In Year 1, the team developed integrated technologies and finalized the pilot test parameters. This involved finalizing the plan for placing sensory devices and common parcel locker systems on public and private property; issuing the request for proposals; selecting vendors; and gaining approvals necessary to execute the plan. The team also developed techniques to preprocess the data streams from the sensor devices, and began to design the prototype smart phone parking app to display real-time load/unload space availability, as well as the truck load/unload space behavior model.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). Year One Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.

Urban Delivery Companies’ Needs and Preferences for Green Loading Zones’ Implementation: A Case Study of NYC

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URL: https://doi.org/10.1061/9780784484340

Publication: International Conference on Transportation and Development 2022

Publication Date: 2022

Summary:

Green loading zones (GLZs) are curb spaces dedicated to the use of electric or alternative fuel (“green”) delivery vehicles. Some US cities have begun piloting GLZs to incentivize companies to purchase and operate more green vehicles. However, there are several questions to be answered prior to a GLZ implementation, including siting, potential users, and their willing to pay. We reviewed best practices for GLZs around the world and surveyed goods delivery companies operating in New York City to collect such information for a future GLZ pilot. The findings suggest the best candidate locations are areas where companies are currently subject to the most parking fines and double parking. Companies expressed willingness to pay for GLZs, as long as deploying green vehicles in the city can offset other cost exposures. Respondents also selected several single-space GLZs spread throughout a neighborhood as the preferred layout.

Authors: Thomas MaxnerNota GoulianouDr. Andisheh RanjbariDr. Anne Goodchild

Technical Report

Cost, Emissions, and Customer Service Trade-Off Analysis In Pickup and Delivery Systems

URL: https://rosap.ntl.bts.gov/view/dot/24904

Publication: Oregon Department of Transportation, Research Section

Publication Date: 2011

Summary:

This research offers a novel formulation for including emissions into fleet assignment and vehicle routing and for the trade-offs faced by fleet operators between cost, emissions, and service quality. This approach enables evaluation of the impact of a variety of internal changes (e.g. time window schemes) and external policies (e.g. spatial restrictions), and enables comparisons of the relative impacts on fleet emissions. To apply the above approach to real fleets, three different case studies were developed. Each of these cases has significant differences in their fleet composition, customers’ requirements, and operational features that provide this research with the opportunity to explore different scenarios.

The research includes estimations of the impact on cost and CO2 and NOX emissions from fleet upgrades, the impact on cost, emissions, and customer wait time when demand density or location changes, and the impact on cost, emissions, and customer wait time from congestion and time window flexibility. Additionally, it shows that any infrastructure use restriction increases cost and emissions. A discussion of the implications for policymakers and fleet operators in a variety of physical and transportation environments is also presented.

Authors: Dr. Anne Goodchild, Felipe Sandoval

Recommended Citation:
Goodchild, A., & Sandoval, F. (2011). Cost, Emissions, and Customer Service Trade-Off Analysis In Pickup and Delivery Systems (No. OR-RD 11-13). Oregon Department of Transportation Research Section.