Urban Mobility Planning and Path Optimization in Mobility as a Service (MaaS) Using Machine Learning

Mobility as a Service (MaaS) is a novel concept that aims to provide seamless and sustainable urban mobility solutions to users. In this study, we propose a machine learning-based approach for urban mobility planning and path optimization in MaaS. Our approach leverages historical trip data to predict travel demand and uses a combination of graph theory and machine learning techniques to optimize the routes and schedules of various modes of transportation. We demonstrate the effectiveness of our approach through simulations conducted on a real-world urban transportation network. Our results show that our approach can significantly reduce travel time, improve accessibility, and enhance the overall user experience.