Exploring Delivery Services Substituting Household Shopping Trips: Implications for Travel, Transportation Networks, and Fleet Optimization, and Insights on the Potential of Autonomous Vehicles

This dissertation delves into the intersection of two critical elements shaping the future of transportation: the necessity to anticipate and explore the forthcoming transportation paradigm with the new possibilities offered by Autonomous Vehicles (AVs), and the challenges and opportunities presented by shopping delivery services, particularly same-day delivery (SDD). This study investigates the transformative potential of SDD services facilitated by a fleet of shared autonomous vehicles (SAVs). With a dual focus on both the network and household layers, the dissertation addresses the viability of SDD services encompassing impacts on travel patterns, vehicle miles traveled (VMT) savings, and operational strategies for efficient fleet management in one side, as well as the impacts on travel patterns. Leveraging real-world data for the Irvine network and employing optimization methodologies, it investigates the potential VMT savings compared to the base scenario where households conduct their own shopping activities, analyzes the optimal fleet size needed to achieve significant VMT reductions, and evaluates operational strategies for cost-effective and efficient service delivery. It analyzes the optimal fleet size and system design settings needed to achieve significant VMT reductions without losing profitability and evaluates operational strategies for cost-effective and time-sensitive service delivery.

The network layer is modeled as a multi-Vehicle and Multi-Depot Pickup and Delivery Problem with Time Windows (m-MDPDPTW), implemented in Google OR-Tools. An analysis is presented for a delivery service comprising an AV fleet serving households on their daily shopping trips for the case study of Irvine. The results indicate these services can significantly decrease the distance traveled and the time spent for shopping trips. The study tests several scenarios involving varying percentage of the service demand, time window for deliveries, loading/unloading time, and depots distribution are considered. The household layer analysis is based on the California Statewide Travel Demand Model (CSTDM) data for the Irvine population, with travel time saved as the accessibility measure. Using the Household Activity Travel Pattern Problem (HAPP), formulated as a pickup and delivery problem with time windows for household daily activities, this measure is compared over different scenarios, to shed light on new opportunities in travel and activity planning enabled by AVs. High Performance Computing is used to make the NP-Hard HAPP’s application possible for a large-scale case study.