Sustainable Freight Research Center

Electric vehicle (EV) depot charging increases the feasibility for fleet operators to convert fleets from internal combustion engine vehicles to zero-emission vehicles (ZEVs). This study considers two example cases: a fleet of medium-duty delivery trucks and a fleet of heavy-duty short-haul trucks. In both cases, trucks are charged at a depot by direct current (DC) fast chargers (50 kW, 150 kW, or 350 kW), and we estimate charging infrastructure cost as a function of the EV fleet size. Results indicate that per-vehicle infrastructure cost will decrease substantially as the fleet size increases, though infrastructure cost is very sensitive to charger utilization rates. The higher the charger utilization, the lower the infrastructure cost will be, as the depot will need fewer chargers installed given a certain number of vehicles being charged. Therefore, one cost reduction strategy is to improve daily utilization rates to reduce the charger count demand and eventually reduce the infrastructure cost (the capital cost). Finally, results show that the annualized infrastructure cost is dwarfed by the annual cost of the electricity dispensed to the EV fleet.

E-commerce can potentially make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities, particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include the use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection-points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

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The expansion of automation in the U.S. economy is increasingly tangible and will presumably entail positive and negative impacts that are not yet well understood. In the freight sector, there is uncertainty about how and when automation will impact labor. Beyond this, there are further unknowns about what the impacts will be on such freight subsectors as warehousing, long- and short-haul. It is expected that penetration rates of freight automation will vary across subsectors. In some subsectors, new jobs will be created and/or working conditions will improve. Other subsectors will see declining job quality and/or job losses that require workers to transition to new roles or sectors entirely, when possible. Changes in job opportunities and quality will vary within sectors and subsectors, by region, and/or by firm. This study offers an overview and recommendations in three directions. First, despite the uncertainties and based on past and present examples of automation, it provides some insights about strategies that may help impacted workers within and outside of the heavy freight sector transition. Second, it discusses examples of existing public policies that can support a transition for automation-impacted workers. And third, it provides insights on how different freight subsectors are likely to be impacted by automation.

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This project aims to study the impacts of e-commerce on shopping behaviors and related externalities. The objectives are divided into five major tasks in this project. Methods used include Weighted Multinomial Logit (WMNL) models, time series forecasting, and Monte Carlo (MC) simulations. The American Time Use Survey (ATUS) and the National Household Travel Survey (NHTS) databases are used for identifying the independent and dependent variables for behavioral modeling. At the same time, the researchers collected all MSA population data from the U.S. Census Bureau and combined the shares of each variable from ATUS to generate a synthesized population, which serves as input into the MC simulation framework together with the behavioral model. This simulation framework includes the generation of shopping travel parameters and the calculation of negative externalities. The authors do this to estimate e-commerce demand and impacts every decade until 2050. The results and analyses provide information that supports the generation of shopping travel and the estimations of a series of negative externalities using MC simulation, which includes shopping travel parameters, last-mile delivery parameters, and emission rate per person. For different parameters, a unique probability distribution or a regression relation is obtained for different MSAs, and this distribution is fed into the subsequent MC simulation. Finally, the researchers simulated shopping behaviors for synthesized populations (until 2050) and to estimate the expected negative externalities. The MC simulation generates aggregate average vehicle miles traveled (VMT) and emissions (negative externalities) for different shopping activities in the planning years and different MSAs.

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Carbon emissions targets require large reductions in greenhouse gases (GHGs) in the near-to mid-term, and the transportation sector is a major emitter of GHGs. To understand potential pathways to GHG reductions, this project developed the U.S. Transportation Transitions Model (US TTM) to study various scenarios of zero-emission vehicle (ZEV) market penetration in the U.S. The model includes vehicle fuel economy, vehicle stock and sales, fuel carbon intensities, and costs for vehicles and fuels all projected through 2050. Market penetration scenarios through 2050 are input as percentages of sales for all vehicle types and technologies. Three scenarios were developed for the U.S.: a business as usual (BAU), low carbon (LC), and High ZEV scenario. The LC and High ZEV include rapid penetration of ZEVs into the vehicle market. The introduction of ZEVs requires fueling infrastructure to support the vehicles. Initial deployments of ZEVs are expected to be dominated by battery electric vehicles. To estimate the number and cost of charging stations for battery electric trucks in the mid-term, outputs were used from a California Energy Commission (CEC) study projecting the need for chargers in California. The study used the HEVI-Pro model to estimate electrical energy needs and number of chargers for the truck stock in several California cities. The CEC study outputs were used along with the TTM model outputs from this study to estimate charger needs and costs for six U.S. cities outside California. The LC and High ZEV scenarios reduced carbon emissions by 92% and 94% in the U.S. by 2050, respectively. Due to slow stock turnover, the LC and High ZEV scenarios contain significant numbers of ICE trucks. The biomass-based liquid volume reaches 70 (High ZEV) to 80 (LC) billion GGE by 2045. For the cities in this study, the charger cost ranges from $5 million to $2.6 billion in 2030 and from roughly $1 billion to almost $30 billion in 2040.

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California is in the midst of improving its freight system. For example, the California Sustainable Freight Action Plan (CSFAP) established the goal of reaching a 25% increase in freight efficiency, the use of 100,000 zero emission vehicles and equipment (and maximize the number of near zero emission vehicles) in the system, and improving economic competitiveness. Although there are multiple strategies and approaches to help achieve these goals, this study focuses on analyzing the factors to foster the adoption of zero and near-zero emission vehicles. For example, the use of monetary and non-monetary incentives to elucidate behavioral changes (e.g., fleet purchase decisions). This study considered compressed (renewable) natural gas (CNG/RNG), hybrid electric (HE), battery electric (BE) and fuel-cell hydrogen (H2) vehicles. The research team collected information through a web-based stated preference survey sent (in two waves) to fleets and carrier companies to gather data about their economics, and their vehicle purchase preferences. However, the response rate was very small which limited the type of analyses conducted with the data. Alternatively, the study team developed a multi-criteria decision-making tool using a Spherical Fuzzy Analytical Hierarchy Process based on experts’ knowledge. The approach considered the variability in the technical and operational characteristics, market readiness, and other factors related to these technologies. The model helped provides insights about the most appropriate options for different uses (e.g., last mile, long-haul distribution). Specifically, the authors evaluate the alternatives using five criteria: economic; business, incentives & market-related; environmental & regulatory; infrastructure; and safety & vehicle performance factors. The analyses also consider twenty-one sub-criteria, e.g., total cost of ownership, payback period, brand image, financial & non-financial incentives, and public/private fueling/ charging infrastructure availability.

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In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. While this has brought prosperity for the e-retailers, the ever-increasing consumer demand has brought more trucks to the residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. To cope with this, different logistics strategies such as the introduction of micro-hubs, alternative delivery points, and use of cargo bikes and zero emission vehicles for the last mile have been introduced and, in some cases, implemented as well. This project, hence, aims to develop an analytical framework to model urban last mile delivery. In particular, this study will build upon the previously developed econometric behavior models that capture e-commerce demand. Then, based on continuous approximation techniques, the authors will model the last-mile delivery operations. And finally, using the cost-based sustainability assessment model (developed in this study), the authors will estimate the economic and environmental impacts of residential deliveries under different city logistics strategies.

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The freight system is a key component of California’s economy, but it is also a critical contributor to a number of externalities. Different public agencies, private sector stakeholders, and academia engaged in the development of the California Sustainable Freight Action Plan (CSFAP). This plan put forward a number of improvement strategies/policies. However, the freight system is so complex and multifaceted, with a great number of stakeholders, and freight operational patterns, that evaluating or assessing the potential impacts of such strategies/policies is a difficult task. To shed some light, this project develops a freight system conceptualization and impact assessment framework of the freight movements in the State. In doing this, the framework assesses the impact of commodity flows from different freight industry sectors along supply chains within, originating at, or with a destination in the state of California.

The conceptual framework analyzes the freight flows in supply chains, and the type of freight activity movements and modes. The framework uses a Life Cycle Assessment (LCA) Methodology. The framework could be extended to support multidimensional cost/benefit appraisals for both direct benefits (e.g., delays, costs, accidents, maintenance) and social benefits to non-users which include impacts on regional and national economies as well as environmental and health impacts. This report discusses the main components of the conceptual framework based on a comprehensive review of existing methodologies. The implementation is limited to the Life Cycle Impact Assessment (LCIA) following the Environmental Protection Agency’s Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI).

The report describes the results from the LCIA implementation for a number of case studies. Specifically, the work estimated the impacts of moving a ton of cargo over a mile for various industry categories and commodity types. These results show the relative difference across industries and commodities and could serve to identify freight efficiency improvement measures in the state of California.

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