Using Machine Learning and AI to explore potential systems for costing and managing Mobility as a Service and Transport Infrastructure

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Transport and in particular city congestion are huge issues in the 21st Century, with gridlock and pollution costing global economies billions annually. Using data sets around congestion, pollution and road safety it is possible to calculate the real cost of driving, and its impact on the environment, pedestrians, cyclists and other commuters. The project will assess the technical feasibility of a Holistic Transport Costing (HTC) Model of intelligent "pay-per-mile" micro-transactions where vehicles have to pay more to access busy or dangerous roads but are rewarded for taking more environmentally friendly routes, or carrying more passengers. The system would use machine learning (ML) and artificial intelligence(AI) to continually intelligently negotiate road usage costs between autonomous agents on behalf of the city and cars then later autonomous vehicles. The system would be capable of continual evolution to reflect priorities and behaviours in a single city, ‘evolving’ it to become less congested, more efficient, safer and cleaner. The platform would also be adaptable to multi-modal transport journeys, integrating different transport options as data around times, pricing and access became available.