Visualizing on Transit Networks Capacity, Crowding & Reliability

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Presentation transcript:

Visualizing on Transit Networks Capacity, Crowding & Reliability INRO Model City Seminar May 2016 Gurbani Paintal

Capacity, Crowding & Reliability Measures Implemented in the Greater Golden Horseshoe Model version 4 Custom procedure in Python Visualized within Emme Uses Extended Transit Assignment for multiple mode groups (classes) Outputs Level of Service attributes in minutes Inputted into the Mode Choice model Iterative process: Able to change paths within a mode group Iterates with the mode choice model

Passenger Volumes

Capacity Penalty at Nodes (unable to board)

Crowding Penalty

Extra Average Wait Time (unreliability at nodes due to bunching)