The hyper run assignment model. Simulation on a diachronic graph of congested transit networks with fail-to-board probabilities at stops

Modelling the within-day dynamic of passenger flows is crucial to optimize the service quality of public transport systems when supporting real-time operations and providing predictive information, as well as for off-line planning. Recurrent and non-recurrent congestion phenomena are increasingly affecting densely connected transit networks. In particular, the measures adopted to contain the spread of the COVID-19 pandemic affect significantly public transport capacity. Therefore, transit operators require a tool that can quickly forecast real-time capacity issues in the transit system to perform service recovery (e.g. introducing new runs) and to inform passengers about crowding at stops (e.g. through real-time information panels or trip planners). This research proposes an innovative congested run-based macroscopic dynamic assignment model, which simulates service degradation for passengers mingling at stops and strict capacity constraints. Fail-to-board probabilities are introduced at the diversion nodes of a diachronic hypergraph to represent service performances and passenger volumes on each run, in the framework of an implicit route enumeration model. This last aspect, jointly with the careful containment of the space-time network dimension, makes the model suitable for real-time applications in terms of computation time.