Hybrid genetic algorithm for an on-demand first mile transit system using electric vehicles

• Main Authors: Perera, Thilina, Prakash, Alok, Gamage, Chathura Nagoda, 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; Demand Responsive Transit
• Online Access: https://hdl.handle.net/10356/147728
• Institution: Nanyang Technological University

First/Last mile gaps are a significant hurdle in large scale adoption of public transit systems. Recently, demand responsive transit systems have emerged as a preferable solution to first/last mile problem. However, existing work requires significant computation time or advance bookings. Hence, we propose a public transit system linking the neighborhoods to a rapid transit node using a fleet of demand responsive electric vehicles, which reacts to passenger demand in real-time. Initially, the system is modeled using an optimal mathematical formulation. Owing to the complexity of the model, we then propose a hybrid genetic algorithm that computes results in real-time with an average accuracy of 98%. Further, results show that the proposed system saves travel time up to 19% compared to the existing transit services.