Unravelling the last-mile conundrum: A comparative study of autonomous delivery robots, delivery bicycles, and light commercial vehicles in 14 varied European landscapes

Abstract

In this study, the growing need for efficient delivery services in the expanding e-commerce sector is addressed, with a focus on real-life consumption data. A comprehensive modelling framework is proposed to evaluate the efficiency of various transportation modes, including Light Commercial Vehicles (LCVs), cargo bicycles, and Autonomous Delivery Robots (ADRs). Utilizing the Google API, delivery destinations are identified, origin-destination matrices are created, and routes are optimized using Google OR-Tools and a capacitated vehicle routing problem solver. The study's robustness is further enhanced by incorporating real-life consumption data, considering diverse European contexts, varying urban scales, traffic patterns, and topographical factors, thus assessing their impact on transportation efficiency. The findings reveal that ADRs are efficient in pedestrian-focused, traffic-limited areas, while bicycles are more effective in dense city centres. This research highlights the necessity of tailoring transportation mode choice to specific urban characteristics for optimal efficiency and consumer satisfaction.Overall, the present study offers valuable insights into optimizing delivery services in different urban settings, providing a significant model for improving last-mile delivery systems. It contributes to understanding how different transportation modes can be effectively integrated into urban logistics, addressing environmental sustainability, operational efficiency, and real-life consumer demands.