Research Team: Miguel Jaller (lead), Leticia Pineda, and Hanjiro Ambrose
UC Campus(es): UC Davis
Problem Statement: Urban goods delivery is an essential component of the greater freight system and vital to the urban economy. While urban goods delivery represents a small share of urban traffic, it generates a disproportionate amount of pollution and greenhouse gas emissions. In many ways, the dis-benefits are increasing, with the current transition to on-demand delivery. Unfortunately, urban goods delivery is not well understood, and newly-proliferating on-demand delivery services even less so. A clearer understanding is needed so that policies can be designed and implemented to improve the efficiency and environmental footprint of the urban freight system, including the introduction of zero and near-zero emission vehicles—a strategy embedded in the Governor’s Sustainable Freight Action Plan as well as CARB’s AB 32 Scoping Plan, Statewide Implementation Plan, and Mobile Source Strategy.
Project Description: This work conducts an empirical assessment of the economic and driving patterns of trucks used for last mile delivery given the increase in these vehicles serving even more densely populated areas (compared to the long-haul transport). The work concentrates on parcel deliveries, as they are typically used to transport the goods resulting from the rapidly growing e-commerce demand. The authors evaluate the performance by analyzing real driving data from parcel fleets (Walkowicz et al., 2014; Jaller et al., 2017a), and use the data to conduct life-cycle assessments (LCA) to estimate the various impacts. The contributions of the work are: 1) comparison analyses between parcel delivery driving data with other delivery vocations to identify different freight patterns. The analyses show the differences and similarities between the driving patterns when using different drivetrains for a number of parcel delivery vocations. 2) Estimation of delivery tour length distributions (TLDs), and specific fuel consumption (SFC) for different drivetrains and vehicle classes. And, 3) estimate the total cost of ownership (TCO), including externalities, of different truck technologies under numerous scenarios that assume changes in fuel efficiency and incentives of certain drivetrains. Additional sensitivity analyses are conducted to identify the key parameters that affect the TCO. Among these, the analyses show the efficiency of purchase and use incentives for these technologies. The results can be extrapolated to a system-wide scope for similar vocations with common operational variables and explore the benefits and costs of transitioning to zero-emission technologies.