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.
17-mar-2016
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ICAR/05 - TRASPORTI
English
Con Impact Factor ISI
transit assignment; mesoscopic simulation-based assignment; run-oriented assignment; strategy-based transit path choice models; real-time predictive traveller information
http://www.tandfonline.com/doi/full/10.1080/15472450.2016.1164047
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].
Nuzzolo, A; Crisalli, U; Comi, A; Rosati, L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/142219
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