Connectivity-Aware Route Design and Frequency Setting for Connected and Automated Shuttles

The advent of Connected and Automated Shuttles (CAS) offers a transformative approach to public transportation, promising enhanced efficiency, safety, and user experience through a tailored service. This paper presents a multi-objective optimization framework for CAS systems, focusing on two critical dimensions: connectivity Quality of Service (QoS) and User Travel Demand (UTD). By integrating these objectives, we aim to develop a balanced and effective CAS deployment strategy that maximizes operational efficiency while meeting diverse user needs. The optimization framework employs a hybrid approach that combines simulation-based modeling, heuristic algorithms, and multi-objective evolutionary techniques.We proposed three case studies representing urban transit scenarios of different sizes and complexity to demonstrate the framework’s applicability and effectiveness. We have achieved median values of hypervolume up to 880 representing a wide range of solutions going from full connectivity to serving the maximum number of passengers according to the shuttles’ capacity. Trade-off values for our three case studies was higher than 0.9 in the 83% of the studied cases.