Extension of the hyper run assignment model to real-time passengers forecasting in congested transit networks based on count data.

Recurrent and non-recurrent congestion phenomena increasingly affect densely interconnected transit networks. In particular, the measures adopted to contain the spread of the COVID-19 pandemic significantly affect public transport capacity, increasing congestion. Typical congestion phenomena, together with service disruptions and atypical demand, can lead to low levels of service harming planned schedules. Therefore, transit operators require a tool that can quickly forecast a potential lack of capacity in transit systems, to perform service recovery (e.g., introducing new runs) and inform passengers about crowding (e.g., through real-time information panels or trip planners). This research proposes an innovative congested run-based macroscopic dynamic assignment model that incorporates real-time measurements and events to compute users’ elastic route choices under the assumption that passengers are fully informed. The model simulates the effects of congestion events and countermeasures introduced by the operators, allowing them to test several scenarios on large transit networks faster than in real-time.