MobiliTwin: from predictive to prescriptive analytics

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This innovative project focuses on developing a comprehensive platform to optimize routing and scheduling in public transportation. The solution will provide a data-driven AI platform to design and run an optimised end-to-end bus service for a region, served by a combination of Dynamic Responsive Transit (DRT) buses and traditional timetabled bus services. Importantly, this AI-powered optimisation of these two types of bus service delivery will provide a holistic bus solution which can serve a modern region's population: both in dense city and regional rural areas, providing inclusive and accessible transit for all. This will improve efficiency and decision-making for enhanced service delivery in a challenging environment where the balance of transit needs within cities and across less-densely populated regions is constantly in flux. Leveraging a digital twin and reinforcement learning, the solution will generate passenger demand using Reinforcement Learning (RL) based on historical data, road traffic, and event information to create a digital twin. The digital twin will closely mimic real-world demand by generating large-scale data and employing AI for training. The project also includes the implementation of a reinforcement learning-based optimal routing model. Continuous learning using demand prediction data and real operational data will drive ongoing model evolution. Additionally, the project focuses on achieving fast response performance and real-time optimal routing through model lightweighting, ultimately culminating in the development of a digital twin powered by these innovative techniques. The co-developed solution will be built on top of existing digital twin, bus routing and mobility platforms by project partners Ciel & Alchera. The project partners have established commercial success in productising advanced Machine Learning and already sell solutions to the target customers of this collaborative R&D, with a clear roadmap to commercialisation of this innovation.