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Mass Transit Optimization Key Capabilities
Investigate optimal transfer strategies
- Hub & spoke (e.g. bus feeders & light rail trunks)
- Point-to-point (e.g. taxis, vanpools)
Demand-responsive dynamic vehicle routing
- Creates unique schedule based on demand inputs
- Utilizes command, control, and monitoring networks
- Emphasizes passenger service quality – high throughput, low latency, minimal vehicle movement
Apply transit system constraints
- Vehicle size (seating capacity)
- Station size (berthing capacity)
- Link connectivity (network topology)
Multimodal layers of vehicles
- various passenger capacities or network connectivity
Notes:
“Framework” indicates that it's neither complete nor do we exercise all of its potential functionality
Similar prior works:
SimCity: spent lots of time researching; ingrained with few common modes of transit, no vehicle persistence; difficult to collect full data
PRT analysis:
John Lees-Miller 2003: SATURN (Simulation and Analysis Tools for Urban automated Rapid transit Networks): high school student's Java simulation
SimPyTran 2004: continuous time comparison of station throughput of PRT vs. light rail
Mass transit: (Jayakrishna's students)
Cristian Cortes 2003 HCPPT
Louis Pages MTVRP 2006: paper in NAS's Transportation Research Board; similar formulation