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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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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In the last decade, e-commerce has grown substantially and transformed individual shopping behaviors. Most shopping activities—at least part of the search, if not the purchase itself—now involve an online component. This has consequently changed commodity flow and urban goods distribution. E-commerce has the potential to reduce the negative impacts of shopping on the environment by substituting individual shopping trips to stores using personal cars with optimized truck deliveries. However, shopping behavior is often more complex than this one-to-one substitution.

Additionally, e-retailers entice consumers with free shipping, free returns, same-day, one-hour or two-hour expedited deliveries, and more in a quest for increased market share. These enhanced services result in additional distances driven, emissions, and operational costs for the e-retailer. The increasing customer expectations around lead time, delivery time, and return policy present a need for more sustainable delivery options, particularly for the “last mile” between the distribution center and the customer. Last-mile operators are considering alternatives to traditional diesel truck-based, door-to-door delivery such as use of alternative fuel (e.g., electric) vehicles, delivery from micro-hubs using cargo bikes, customer pickup at collection points, and crowdsourced deliveries. Researchers at the University of California, Davis developed models for e-commerce demand, last-mile delivery operations, and cost and sustainability assessment, then applied this modeling framework to a case study in Southern California to evaluate the potential impact of these strategies under different delivery scenarios. This policy brief summarizes findings from that research, along with policy implications.

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The growth of e-commerce, spurred by the internet, has transformed urban goods flow. What would previously have been a trip to a store is now a hassle-free delivery to the home. With consolidated and optimized delivery tours, e-commerce has the potential to make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused service, urban freight witnesses a significant increase in associated distribution costs and negative externalities including greenhouse gas emissions advancing global climate change, as well as criteria pollutant emissions worsening local air quality and thus affecting those living close to logistics clusters. Thus, considering the potential of e-commerce to render economically viable, environmentally efficient, and socially equitable urban goods flow, it is pertinent to understand the opportunities and challenges associated with urban freight in light of the increasingly consumer-focused e-commerce distribution. To this end, the author develops A) the impact of e-commerce on urban goods distribution, with a simulation framework founded on consumer shopping behavior simulating urban goods flow, B) the impact of key delivery environment parameters on e-commerce goods distribution, with a continuous approximation (CA) framework modeling last-mile distribution operation for an e-retailer, and C) the impact of demand uncertainty on e-commerce goods distribution, with a discrete optimization framework formulating 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 using an adaptive large neighborhood search (ALNS) metaheuristic algorithm.

Researchers at UC Davis explored what an automated vehicle future in the San Francisco Bay Area might look like by simulating:

1) A 100% personal automated vehicle future and its effects on travel and greenhouse emissions.

2) The introduction of an automated taxi service with plausible per-mile fares and its effects on conventional personal vehicle and transit travel.

The researchers used the Metropolitan Transportation Commission’s activity-based travel demand model (MTC-ABM) and MATSim, an agent-based transportation model, to carry out the simulations. This policy brief summarizes the results, which provide insight into the relative benefits of each service and automated vehicle technology and the potential market for these services.

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In much in the same way that the automobile disrupted horse and cart transportation in the 20th century, automated vehicles hold the potential to disrupt our current system of transportation in the 21st century. Experts predict that vehicles could be fully automated by as early as 2025 or as late as 2035. Methods are needed to help the public and private sector understand automated vehicle technologies and their system-level effects. First, we explore the effects of automated vehicles using the San Francisco Bay Area Metropolitan Transportation Commission’s activity-based travel demand model (MTC-ABM). The simulation is unique in that it articulates the size and direction of change on travel for a wide range of automated vehicles scenarios. Second, we simulate the effects of the introduction of an automated taxi service on conventional personal vehicle and transit travel in the San Francisco Bay Area region and use new research on the costs of automated vehicles to represent plausible per mile automated taxi fares. We use an integrated model for the San Francisco Bay Area that includes the MTC-ABM combined with the agent-based MATSim model customized for the region. This model set uses baseline travel demand data from the region’s official activity-based travel model and dynamically assigns vehicles on road and transit networks by the time of day. Third, we use the MTC-ABM and the MATSim dynamic assignment model to simulate different “first” mile transit access services, including ride-hailing (Uber and Lyft) and ridesharing (Uber Pool/Lyft Line and Via) with and without automated vehicles. The results provide insight into the relative benefits of each service and automated vehicle technology and the potential market for these services.

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