Autonomous Ride-Hailing Services: A Scalable Heuristic Approach for Efficient Transportation

One significant aspect of smart cities is the emergence of Autonomous Electric Vehicles (AEVs), which are poised to revolutionize transportation systems, offering an intelligent transportation system that surpasses traditional modes in terms of reducing carbon emissions, environmental pollution, congestion, transportation costs, and wait times. In this context, ride-hailing systems will play a pivotal role by leveraging shared AEVs to optimize transportation efficiency. This paper presents an in-depth exploration of an efficient Autonomous Driving System (ARH) that leverages a fleet of autonomous electric vehicles, enabling riders to seamlessly schedule trips while the intelligent system allocates suitable vehicles and integrates multiple ride requests. Our proposed solution aims to minimize arrival times for both AEVs and passengers by accepting the maximum number of feasible requests. To strike a balance between passenger satisfaction and system profitability, this study introduces a novel heuristic approach called Minimization of Passenger and Vehicle Time (MPVT). By adopting this approach, we aim to overcome the challenges associated with complexity and deliver a satisfactory experience for passengers while maximizing profitability for the system operator.