SUMMARY

The project developed accurate and practical prediction models for rail crowding using an intelligent data-driven modelling method.  These models can be merged into the existing software tools to provide customers with detailed crowd prediction information before they and support better decision-making.

The research demonstrated how techniques such as non-parametric forecasting, fuzzy rule based systems and data classification and clustering, can be used to accurately predict daily crowding patterns

Crowding has a major impact on the customer experience which is one of the rail industry 's critical challenges. This research proposed a modelling framework to predict the crowding levels on trains, which will help passengers to plan their journey strategically.