Genetic algorithm based EV scheduling for on-demand public transit system

• Main Authors: Perera, Thilina, Prakash, Alok, Srikanthan, Thambipillai
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Intelligent Transport Systems; Evolutionary Computation
• Online Access: https://hdl.handle.net/10356/147722
• Institution: Nanyang Technological University

The popularity of real-time on-demand transit as a fast evolving mobility service has paved the way to explore novel solutions for point-to-point transit requests. In addition, strict government regulations on greenhouse gas emission calls for energy efficient transit solutions. To this end, we propose an on-demand public transit system using a fleet of heterogeneous electric vehicles, which provides real-time service to passengers by linking a zone to a predetermined rapid transit node. Subsequently, we model the problem using a Genetic Algorithm, which generates routes and schedules in real-time while minimizing passenger travel time. Experiments performed using a real map show that the proposed algorithm not only generates near-optimal results but also advances the state-of-the-art at a marginal cost of computation time.