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

2. 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

3. Passenger Volumes

4. Capacity Penalty at Nodes (unable to board)

5. Crowding Penalty

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