Shared lightweight autonomous vehicles for urban food deliveries: A simulation study

Abstract

In recent years, the rapid growth of on-demand deliveries, especially in food deliveries, has spurred the exploration of innovative mobility solutions. In this context, lightweight autonomous vehicles have emerged as a potential alternative. However, their fleet-level behavior remains largely unexplored. To address this gap, we have developed an agent-based model and an environmental impact study assessing the fleet performance of lightweight autonomous food delivery vehicles. This model explores critical factors such as fleet sizing, service level, operational strategies, and environmental impacts. We have applied this model to a case study in Cambridge, MA, USA, where results indicate that there could be environmental benefits in replacing traditional car-based deliveries with shared lightweight autonomous vehicle fleets. Lastly, we introduce an interactive platform that offers a user-friendly means of comprehending the model's performance and potential trade-offs, which can help inform decision-makers in the evolving landscape of food delivery innovation.

Figures (14)

• Figure 1: Diagram that depicts the process of food delivery orders.
• Figure 2: Diagram for the depiction of the structure and interdependencies among the agent-based simulation layers: A) Urban infrastructure, B) Delivery vehicle fleet, C) User demand
• Figure 4: Diagram of the Finite State Machine (FSM), representing the behavior and transitions between states of the food delivery orders that have been placed by the consumers.
• Figure 5: Simplified diagram of the synthetic database generation process for obtaining the fine-grained food delivery demand dataset.
• Figure 6: Top: Heat map illustrating the spatial density of trip origins (left, restaurants) and destinations (right), with areas of highest density represented in violet. The map also shows the boundary of the study area, which encompasses Cambridge, MA, USA. Bottom: Demand profile of food delivery orders in the study area (Cambridge, MA, USA) demonstrating the temporal distribution of orders throughout the day, aggregated by time intervals of 7.5 minutes.