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- W2325349479 abstract "ABSTRACTThis article 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 modeling 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 modeling 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." @default.
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- W2325349479 date "2016-05-24" @default.
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- W2325349479 title "A mesoscopic transit assignment model including real-time predictive information on crowding" @default.
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- W2325349479 doi "https://doi.org/10.1080/15472450.2016.1164047" @default.
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