Strategic Design of Long-Distance Connected and Autonomous Transportation Systems

The emergence of Connected and Autonomous Transportation Systems (CATS) is revolutionizing long-distance mobility by enhancing efficiency, safety, and sustainability. This study presents a strategic framework for designing long-distance connected and autonomous transportation networks, addressing critical challenges such as infrastructure optimization, vehicle coordination, communication reliability, and cost efficiency. A mixed-integer linear programming (MILP) model is proposed to determine the optimal placement of connectivity enablers, charging stations, and autonomous vehicle hubs. Additionally, simulation-based analysis evaluates traffic flow, route optimization, and real-time decision-making under dynamic conditions. The findings suggest that integrating connectivity infrastructure with advanced AI-driven traffic management systems significantly improves travel time, energy efficiency, and network resilience. This research contributes to the future of intelligent mobility systems, providing insights for policymakers, urban planners, and transportation engineers to develop sustainable and scalable autonomous networks.