Congestion phenomena (e.g., crowding, service interruptions, and atypical demand) increasingly affect complex and interconnected public transport networks, resulting in low levels of services and harming the planned schedules. As a result, public transport operators need a tool to compensate for recurrent and non-recurrent congestion phenomena by recovering the service (e.g., introducing new runs) and notifying passengers about crowding (e.g., through real-time information systems). This study suggests a model that forecasts the volumes of passengers in transit networks, including the effects of events and real-time disruptions. In particular, the model performs a run-based macroscopic transit assignment, computing the elastic route choices of users under the assumption that passengers are fully informed. Moreover, the model corrects its forecasts using real-time count data. The model can also include countermeasures, allowing the operators to test several recovery scenarios on large transit networks faster than in real time.
Real-Time passengers forecasting in congested transit networks considering dynamic service disruptions and passenger count data
2023-06-14
1209419 byte
Conference paper
Electronic Resource
English
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