Date of Award

3-10-2025

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

First Advisor

Dr. Ignacio Castiñeiras

Second Advisor

Dr. Alan McGibney

Third Advisor

Pio Fenton

Abstract

Europe aims for carbon neutrality by 2050, with transportation currently amount- ing 20% of its total emissions. Motivated by the sustained mass transition to Autonomous Electric Vehicles (AEVs) and by the distributed energy generation and storage of local Smart Energy Communities (SECs), this thesis presents the design, implementation and evaluation of a carbon-neutral, community-based, scalable ride-sharing service. Its models formalise the partition of an AEVs fleet over the SECs of a city, as well as the allocation of trip petitions (TPs) to AEVs, their routing and charging scheduling over a simulated time horizon. These models integrate energy generation, allocation and re-routing constraints to maximise the number of TPs served. The solution approach to implement the models includes the application of Greedy-based Heuristics and Metaheuristics, Reinforcement Learning and Mixed Integer Programming techniques. A parameterised instance generator is developed, aligning existing benchmarks (i.e. Google HashCode) and public datasets (i.e. NYC taxis) to the proposed problem formulations and testing the service under various configurations. The ride-sharing service is proven to scale well, when applied to very large instances, providing fast and competitive results. Overall, this thesis contributes to green transportation by providing a computational efficient, scalable, and environmentally sustainable ride-sharing service.

Access Level

info:eu-repo/semantics/openAccess

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