Meeting Artificial Intelligence and Machine Learning for Rail Passenger Service Planning under Competition.

The shared objectives of this subproject are:

  • O1 UPC-URJC-UCLM. Modelling of competition between operators using static Nash Equilibrium models. Analysis of access charges policies under regular and extreme scenarios. Infrastructure Managers (IMs), such as the Spanish ADIF Alta Velocidad (passengers) and ADIF (passengers and freight), usually aim to recover the costs of the infrastructure from the transport operators.
  • O3 UPC-UCLM.  Modelling the time slot allocation by the Infrastructure Manager (IM) in deregulated markets in both normal and low demand scenarios.
  • O4 URJC-UCLM. Combining operations research methods, machine learning and artificial intelligence to advance the theory and practice of operational transportation system planning and management in competitive scenarios.
  • O7 UCLM. Design of machine learning methods and artificial intelligence for the tactical analysis of the competition in transport systems.

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