This paper presents a mesoscopic transit assignment model suitable for real-time prediction of on-board passenger numbers in transit networks with real-time individual predictive information on travel time components and also including on-board crowding. The path choice modelling framework is based on the reproduction of a travel strategy using random utility models that simulate both choices of departure time at origin and first access stop, and en-route choices of vehicle to board at stops. Such choices are based on attributes anticipated through a learning mechanism, which considers previous experiences and provides real-time predictive information. Within-day dynamic network loading considers vehicle capacity constraints, which allows the explicit modelling of fail-to-board events. Finally, results of an application on a real-size test network show the ability of the model to capture effects of providing individual predicted information on vehicle crowding.
Nuzzolo, A., Crisalli, U., Comi, A., Rosati, L. (2016). A Mesoscopic Transit Assignment Model Including Real-Time Predictive Information on Crowding. JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 20(4), 316-333 [10.1080/15472450.2016.1164047].
A Mesoscopic Transit Assignment Model Including Real-Time Predictive Information on Crowding
NUZZOLO, AGOSTINO;CRISALLI, UMBERTO;COMI, ANTONIO;ROSATI, LUCA
2016-03-17
Abstract
This paper presents a mesoscopic transit assignment model suitable for real-time prediction of on-board passenger numbers in transit networks with real-time individual predictive information on travel time components and also including on-board crowding. The path choice modelling framework is based on the reproduction of a travel strategy using random utility models that simulate both choices of departure time at origin and first access stop, and en-route choices of vehicle to board at stops. Such choices are based on attributes anticipated through a learning mechanism, which considers previous experiences and provides real-time predictive information. Within-day dynamic network loading considers vehicle capacity constraints, which allows the explicit modelling of fail-to-board events. Finally, results of an application on a real-size test network show the ability of the model to capture effects of providing individual predicted information on vehicle crowding.File | Dimensione | Formato | |
---|---|---|---|
A mesoscopic transit assignment model including real time predictive information on crowding_Published.pdf
solo utenti autorizzati
Descrizione: full paper
Licenza:
Non specificato
Dimensione
1.39 MB
Formato
Adobe PDF
|
1.39 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.