École Nationale Supérieure de
Techniques Avancées

Abstract

The advent of Electric Autonomous Vehicle (EAV) technology is reshaping the transportation landscape, with EAV-equipped passenger transport systems becoming an integral part of future mobility. This project explores the intricate field of ride-hailing, a transformative concept that revolutionizes the way individuals access transport through smartphone apps. One of the main challenges lies in optimizing the EAVs allocation for passenger requirements, where distance is not the only criterion. Anticipating future demand becomes essential, avoiding EAVs leaving out potential areas of high demand. This project uses reinforcement learning to solve the passenger transportation problem. Electric grid integration enhances EAVs not only as efficient means of transportation, but also an adaptive energy source during times of peak demand. A comprehensive exploration unfolds through three distinct phases: theoretical background, code comprehension and continuation, and a new approach to solve the problem.