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.