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At least 289 records · Page 16

Vehicle energy consumption estimation using large scale simulations and machine learning methods

Vehicle energy consumption and powertrain operations for future vehicle powertrain technologies are predicted through full-vehicle simulations using Autonomie, a simulation tool developed at Argonne National Laboratory. Over 1 million vehicles, with different powertrain types (conventional and numerous electrified powertrains) and component technologies (advanced engines, batteries, electrical machines, light weighting) are simulated for outputs including vehicle fuel economy and cost. Despite the ability of Autonomic to predict fuel economy given various combination of vehicle inputs, the full-vehicle simulation is very time-consuming. It takes approximately 84 h to simulate for 33,060 vehicles even using distributed computing on a cluster of 128 worker nodes. Moreover, Autonomic is unable to populate a continuous output space through simulation since it only yields outputs with discrete values instead of an input-output relation. This paper proposes a novel large-scale learning and prediction process (LSLPP) via machine learning approaches to answer the need for a continuous space of vehicle fuel economy and a more efficient simulation process. By learning in a supervised fashion to discover structures in data, the machine learning approaches seek to train a model that can explain the data with which we can perform prediction over unseen data. As such, we are able to accomplish analytic continuation on the current discrete space of simulation results. In addition, we present a random sampling -based numerosity reduction algorithm incorporated in LSLPP that reduces simulation runs and improves efficiency of data procurement. We analytically derive a theoretical bound in order to guarantee the effectiveness of our algorithm. We also compare random sampling to stratified sampling and suggest sampling strategies. A user-friendly tool is developed to support the process. Our experimental results confirm that the proposed LSLPP is able to greatly accelerate prediction and analysis of vehicle fuel economy. The output of this study is used by the US government to evaluate the impact of its R&D funding on energy security and CO 2 emission as well as for the setting of Corporate Average Fuel Economy (CAFE) standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Vehicle Electrification Impacts on Energy Consumption for Different Connected-Autonomous Vehicle Scenario Runs

Transportation system simulation is a widely accepted approach to evaluate the impact of transport policy deployment. In developing a transportation system deployment model, the energy impact of the model is extremely valuable for sustainability and validation. It is expected that different penetration levels of Connected-Autonomous Vehicles (CAVs) will impact travel behavior due to changes in potential factors such as congestion, miles traveled, etc. Along with such impact analyses, it is also important to further quantify the regional energy impact of CAV deployment under different factors of interest. The objective of this paper is to study the energy consumption of electrified vehicles in the future for different penetration levels of CAVs deployment in the City of Chicago. The paper will further provide a statistical analysis of the results to evaluate the impact of the different penetration levels on the different electrified powertrains used in the study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Factors Influencing Energy Consumption and Cost-Competiveness of Plug-in Electric Vehicles

The widespread implementation of plug-in electric vehicles (PEVs) will depend on public appreciation of the potential savings in ownership costs that PEVs offer over conventional, internal combustion energy vehicles (ICEVs) and hybrid electric vehicles (HEVs), including fuel savings. This study compares the energy consumption and estimated ownership costs of various technologies for multiple drive cycles in the United States and the European Union; identifies and quantifies the impacts of the main parameters influencing the ownership costs of PEVs in comparison with other powertrains for different timeframes, vehicle classes, and technologies; and assesses under what combinations of parameters the cost of PEVs can be competitive with other powertrains.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High-moisture pelleting of corn stover using pilot- and commercial-scale systems: Impact of moisture content, L / D ratio and hammer mill screen size on pellet quality and energy consumption

Here, high-moisture pelleting of corn stover was tested in pilot- and commercial-scale systems. For the pilot-scale study, a 6.35 mm screen size hammer mill ground corn stover was reconditioned to moisture between 14 and 29.6% (w.b.), and 5, 7 and 9 L/D ratio pellet die were used. At 29% corn stover moisture content and L/D ratios of 5 and 9, the bulk density and durability of the pellets were ≤450 kg/m 3 and <90% and >540 kg/m 3 and >90%, respectively. Increasing the feedstock moisture and L/D ratio increased the pelleting energy. Increasing the hammer mill screen size to 11.11 mm reduced the bulk density but not the durability. The response surface models adequately described the pelleting process (R 2 ≥ 0.88), and analysis of variance showed a strong interaction between the process variables and pellet properties. Commercial-scale testing of high-moisture pelleting using 6.35 mm ground corn stover bales in the moisture range 24–26% (w.b.) produced pellets with a density of >675 kg/m 3 and durability of >98%, whereas increasing the hammer mill screen size to 11.11 mm reduced the bulk density by about 100 kg/m 3 but not the durability. The energy consumption of the commercial-scale unit operations for the high-moisture pelleting process was in the range 98–124 kW h/ton, which is 64–72% less energy than conventional pelleting that requires energy about 350 kW h/ton for biomass drying from 30% (w.b.) to 10% (w.b.) moisture content before pelleting. Pellets produced using high-moisture pelleting met International Organization for Standardization and Pellets Fuel Institute international standards. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

09 BIOMASS FUELS↗

High moisture corn stover pelleting in a flat die pellet mill fitted with a 6 mm die: physical properties and specific energy consumption

The quality and specific energy consumption (SEC) of the biomass pellets produced depend upon pelleting process conditions. The present study includes understanding the effect of feedstock moisture in the range of 28–38% (wet basis [w.b.]) and preheating in the range of 30–110°C at two die speeds of 40 and 60 Hz on the physical properties and SEC. A flat die pellet mill fitted with a 6 mm die was used in the present study. The physical properties of pellets such as moisture content, unit, bulk and tapped density, durability, and expansion ratio and SEC of the pelleting process are measured. The results indicate that the pellets produced have durability values in the range of 87–98%, and unit bulk and tapped density in the range of 670–1100, 375–575, and 420–620 kg/m³. Increasing the feedstock moisture content from 33% to 38% (w.b) decreased the unit, bulk and tapped density by about 30–40%. Increasing feedstock moisture content increased the expansion ratio and decreased the density values. A higher feedstock moisture content of 38% (w.b.) and higher preheating temperature of 110°C resulted in lower density and a higher expansion ratio, which can be attributed to flash off of moisture as the material extrudes out of the die. The SEC was in the range of 75–275 kWh/ton. Higher feedstock moisture content of 38% (w.b.) and a lower die speed of 40 Hz increased the SEC, whereas lower to medium preheating temperature (30–70°C), medium feedstock moisture content of 33% (w.b.), and a higher die speed of 60 Hz minimized the SEC to <100 kWh/ton.

09 BIOMASS FUELS↗

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Breaking the loop: Tackling homoacetogenesis by chloroform to halt hydrogen production-consumption loop in single chamber microbial electrolysis cells

The presence of homoacetogenesis and hydrogen production-consumption loop significantly hinders the efficient hydrogen recovery in single chamber microbial electrolysis cells (MECs). Furthermore, no effective inhibition method has been proposed against homoacetogens hitherto. In this study, we demonstrated the effectiveness of chloroform as a homoacetogen inhibitor in MECs. In small MECs (320 ml) operated in batch mode, chloroform with concentrations of 0.02% (v/v) and 0.03% (v/v) ceased homoacetogenesis with nonfermentable substrate (acetate), enhancing hydrogen yield and cathodic hydrogen recovery from 21% and 14% to 94% and 90%, respectively. Electrochemical performance was not significantly affected as current density (based on anode surface area) only decreased from 18 A/m 2 to 13 A/m 2 with chloroform concentration increased from 0% (v/v) to 0.03% (v/v). However, when using a fermentable substrate (glucose), the inhibition against homoacetogens by 0.01% (v/v) chloroform led to a significant decrease in current density possibly due to the decreased production of acetate, which reduced the substrate concentration for exoelectrogenesis. Nevertheless, in a larger MEC (10 L), using an immobilized sludge culture and higher glucose concentration (56mM), 0.02% (v/v) chloroform enhanced hydrogen production rate from 0 L/L/D to 4.9 L/L/D and current density from 12-16 A/m 2 to 18-21 A/m 2 . Microbial community analysis revealed that the amount of homoacetogenic Acetobacterium spp. was eliminated in the cathodic biofilms and planktonic cells by chloroform. Future improvement of this method, through reducing the residual chloroform concentration and developing more environment friendly inhibitor using similar mechanisms, may lead to the practical application in MECs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Thermal performance and energy consumption validation of an occupied local government office building outfitted with ceiling tile phase change materials

Buildings present an opportunity for energy conservation and the modulation of peak energy demand through controlled Heating, Ventilation, and Air Conditioning (HVAC) energy use. The administrative and office building stock in the United States holds potential to achieve energy and demand savings through retrofits such as insulation, weatherization, and thermal energy storage. Specifically, there is a need to validate passive phase change material (PCM) applications in full scale in aging administrative buildings in the US to evaluate the energy benefits. Aim of this study was to conduct a whole building level thermal and energy validation of an operational building and explore an alternative method for evaluating energy efficiency. To accomplish this, the study employed PCMs in the drop ceiling and carry out an energy audit and on-site measurement of HVAC systems' energy demand and consumption. A full-scale EnergyPlus energy model, modeled by the authors, served as a baseline for evaluation. The results show that calibrated model's envelope temperature measures fall within the accepted errors. HVAC energy simulation results also fall within the accepted errors for monthly and hourly pre- and post- PCM retrofit electricity and natural gas data. The novelty of this study is that it employees energy scales per Heating Degree Hour and Cooling Degree Hour, in contrast to the commonly used Heating Degree Days and Cooling Degree Days as reported in the literature to analyze energy savings. These findings underscore the pivotal role of a calibrated model in assessing the efficacy of a singular energy measure, like a PCM-retrofitted ceiling, in an occupied office building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Life Cycle Assessment of Vehicle Lightweighting: A Physics-Based Model To Estimate Use-Phase Fuel Consumption of Electrified Vehicles

Assessing the life-cycle benefits of vehicle lightweighting requires a quantitative description of mass-induced fuel consumption (MIF) and fuel reduction values (FRVs). We have extended our physics-based model of MIF and FRVs for internal combustion engine vehicles (ICEVs) to electrified vehicles (EVs) including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). We illustrate the utility of the model by calculating MIFs and FRVs for 37 EVs and 13 ICEVs. BEVs have much smaller MIF and FRVs, both in the range 0.04-0.07 L e /(100 km 100 kg), than those for ICEVs which are in the ranges 0.19-0.32 and 0.16-0.22 L/(100 km 100 kg), respectively. The MIF and FRVs for HEVs and PHEVs mostly lie between those for ICEVs and BEVs. Powertrain resizing increases the FRVs for ICEVs, HEVs and PHEVs. Lightweighting EVs is less effective in reducing greenhouse gas emissions than lightweighting ICEVs, however the benefits differ substantially for different vehicle models. The physics-based approach outlined here enables model specific assessments for ICEVs, HEVs, PHEVs, and BEVs required to determine the optimal strategy for maximizing the life-cycle benefits of lightweighting the light-duty vehicle fleet.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Development of new baseline models for U.S. medium office buildings based on commercial buildings energy consumption survey data

Building energy estimation for the building sector under various scenarios are needed for building energy regulation and policy making. This often starts with representative baselines (either empirical baseline or modeled baseline). Commercial Buildings Energy Consumption Survey (CBECS) data is a widely used empirical baseline for U.S. commercial buildings, but none of the existing baseline model are developed to represent the CBECS data. This paper aims to develop new baseline models for the U.S. medium office buildings, which can produce modeled baselines consistent with the CBECS data. Here, we introduced the methodology to create baseline models and the criteria to evaluate the performance of baseline models. The methodology consists of three phases: (1) identification of model inputs, (2) model calibration, and (3) model validation with uncertainty analysis. The evaluation index is the coefficient of variation of the root-mean-square deviation (CV(RMSD)) of site energy use intensities (EUIs) between the modeled baseline and empirical baseline. Then 30 new baseline models for two vintages (pre- and post-1980) and 15 climate zones were created. The evaluation shows that the CV(RMSD) is lower than 0.05 for the modeled baselines produced by the new baseline models. As a comparison, the CV(RMSD) is higher than 0.1 for the existing modeled baselines generated by DOE Commercial Reference Building Models. Further analysis shows that the new baseline models are able to capture the uncertainties of the representative features of existing buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Self-Consumption of Electricity Produced from PV Systems in Apartment Buildings - Comparison of the Situation in Australia, Austria, Denmark, Germany, Greece, Italy, Spain, Switzerland and the USA

Since 2000 grid-connected solar photovoltaic systems have increased their world-wide cumulative capacity more than 300 times to reach 400 GW at the end of 2017. Another doubling is forecasted until 2020 and the further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. A new development to enable residents of multi apartment buildings to commonly use electricity generated by a PV system is a relatively new development and is still facing a lot of administrative and regulatory challenges.

14 SOLAR ENERGY↗

Adaptive prefetching on POWER7: Improving performance and power consumption

Hardware data prefetch engines are integral parts of many general purpose server-class microprocessors in the field today. Some prefetch engines allow users to change some of their parameters. But, the prefetcher is usually enabled in a default configuration during system bring-up, and dynamic reconfiguration of the prefetch engine is not an autonomic feature of current machines. Conceptually, however, it is easy to infer that commonly used prefetch algorithms—when applied in a fixed mode—will not help performance in many cases. In fact, they may actually degrade performance due to useless bus bandwidth consumption and cache pollution, which in turn, will also waste power. We present an adaptive prefetch scheme that dynamically modifies the prefetch settings in order to adapt to workloads

adaptive system↗

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A New Method for Low Cost Production of Titanium Alloys for Reducing Energy Consumption of Mechanical Systems

This project investigated an innovative manufacturing process intended to minimize the cost of production of titanium materials and components, and increase the adoption of Ti components for energy consuming applications, such as automobiles. A key innovation of the proposed manufacturing approach is a novel Ti powder sintering technology for making titanium materials with ultrafine grain microstructure in the as-sintered state with minimum, or an absence, of post-sintering processes. The new sintering technology is termed Hydrogen Sintering and Phase Transformations (HSPT), and constitutes a promising manufacturing technology that can be used to produce titanium (Ti) materials and components in a near-net-shape form, thus also minimizing machining costs. Our objective was to meet, or possibly surpass, the mechanical property levels for ASTM B348 Grade 5 for wrought Ti-6Al-4V. Although specific applications call for varying mechanical property requirements, ASTM B348 was created for the demanding applications of the aerospace industry, and is the established standard for Ti-6Al-4V. While the primary goal was to meet, or exceed this standard, the team also had the goal of demonstrating this could be done at a significantly lower cost of production. Interim goals of the project were to fully develop this novel sintering process, and provide sufficient baseline testing to make the method practical and attractive to industry. By optimizing the process parameters for the sintering of titanium hydride (TiH 2 ) powders in a hydrogen atmosphere and controlling the phase transformations during and after sintering, the HSPT process was expected to reduce the energy consumption, and thus cost, of making Ti alloys and fabricating Ti components. The process was designed such that no high temperature melting is required for producing Ti alloys; little or no post-sintering processing is needed for producing desired microstructures (and therefore enhanced mechanical properties), and finally, minimum machining is needed to fabricate finished Ti components. An energy analysis within this report provides more detail, but calculated values indicate that the HSPT process is less than half as energy intensive as conventional wrought processing, while producing mechanical properties that are comparable. In addition to the energy savings anticipated from the industrial production of Ti components, a second prong of energy savings resides in the use phase of components produced, primarily from use in the transportation sector. Titanium has a number of material qualities appropriate for the auto industry, particularly low mass and corrosion resistance. By reducing the weight of automobiles and other vehicles, energy costs and CO 2 production will be reduced over the lifetime of the vehicles, and components in corrosive environments on vehicles, such as exhaust systems and other under carriage parts, may not have to be replaced during a vehicle’s lifetime. Our analysis indicates that by replacing only 5.6 kg of steel parts in an auto with Ti components across the entire US fleet would save approximately 486 million gallons of gasoline per year. This correlates to a reduction of 3.6 million metric tons of CO 2 per year. The potential for replacing many more of the steel parts in automobiles with lighter weight titanium components is clear. The project was very successful overall, meeting all milestones and surpassing project goals in terms of mechanical properties and microstructures produced. In addition to tensile properties, fatigue properties were emphasized in the project work. Powder metallurgy processes often have porosity to some degree in their final microstructure, and porosity is a well-known cause of crack initiation and low fatigue performance. Although many automobile applications do not undergo fatigue stress regimes, many others do encounter cyclic stress, and design criteria in the latter case require good fatigue properties. Production and testing of HSPT parts showed excellent tensile properties and fracture toughness, and fatigue properties that exceeded all previously reported powder metallurgy Ti methods, overlapping with wrought processed values. Fatigue limits exceeded 500 MPa and tensile strength exceeded 1,000 MPa while maintaining good ductility. Microstructures produced during the project period easily surpassed pre-project expectations. In addition to producing very fine grains in the as-sintered state (without post sintered thermo-mechanical work), porosity was reduced and industrially relevant microstructures previously undemonstrated in any other powder metallurgy titanium method were produced using HSPT materials. These microstructures, both bi-modal and globularized, were produced with simple post-sinter heat treatments, but without the need for energy intensive mechanical work. The employed heat treatments expanded the available mechanical property range (tensile strength vs. ductility) of the HSPT system in Ti-6Al-4V. The project has resulted in the publication, thus far, of five refereed journal articles and five conference proceedings papers, as well as a patent application, two dissertations and a master’s thesis. Two additional journal articles are currently under review, and at least three others are currently in preparation, with several additional students anticipated to graduate within the coming year. Presentations and papers were a particular focus of the second half of the project, once significant experimentation had been performed and analyzed. As part of our efforts to disseminate information of our results, the Ti research teams within Prof. Fang’s and Prof. Chandran’s research groups had a strong presence at the 13th World Conference on Ti, August 16-20, 2015, in San Diego. Several research groups in the US and in Europe are now performing experiments using the HSPT process. Accompanying efforts to bring HSPT to the Ti community at large, and industry in particular, work has continued with our partners and with other interested industrial Ti users and producers, including Boeing and GKN (a major powder metallurgy parts manufacturer). Commercialization has been a central focus of the final phase of the project, and Reading Alloys signed a provisional licensing agreement in summer of 2015. They are currently seeking an appropriate customer with which to pursue initial parts manufacturing efforts. Other licensing options and partners are continuing to be pursued. The promise of lightweight, strong and corrosion resistant Ti alloys with long fatigue lifetimes for automobile or transportation applications has been the vision of the metal industry since titanium came to the attention of scientists and engineers. The sole limitation of realizing these goals has been cost, which is primarily a function of energy used in production. The HSPT process was shown through this work to be capable of realizing this goal, and facilitating the practical use of titanium in US automotive and other industries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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

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: 1. Reduce parking-seeking behavior in the study area by 20%; 2. Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%; 3. 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 neighbourhood 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 analysed 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.

97 MATHEMATICS AND COMPUTING↗