Slide 1

Address 2 questions:
1. How can we evaluate the effectiveness of an urban complex?
- Demand / Sustainment / Measurement framework:
- - Investigates demand distribution patterns influenced by urban planning topology
  - Quantifies effects of transportation infrastructure topology and mode of operation
  - Determines system's ability to satisfy resident / industrial needs
2. What transit paradigms succeed at making the world “smaller”?

CabinTaxi verified and tested in Germany, abruptly abandoned due to NATO commitments
Taxi2000 severed from Raytheon
Morgantown, WVU operational group transit system; abandoned by Boeing
ULTra system slated for 2007 deployment in Heathrow airport, UK and Dubai, UAE

We often search for advanced transportation solutions to energy problems
- We can make larger impacts by reducing travel need/distance by adjusting urban planning and logistics
Urban Layout
- Increase density
- Culminating in arcology concepts
- - Increased density correlated with
  - decreased energy use per capita
Logistics
- Stagger work schedules to reduce peak loads
- Flexibility to optimize residence / workplace pairings
- Mass transit effectiveness that rivals personally-owned vehicles in door-to-door performance
- Enabled by transit-oriented design

Soleri's Arcology
- Architectural implosion of cities
- Form an human relationship to the environment
Dantzig & Saaty's Compact City
- Comprehensive proposal for many aspects of a functioning hyperstructure
Crawford's Carfree Cities
- Reference designs most applicable to transit approach and assumptions used in this thesis

Offer diverse set of specialized skills and jobs
- Well-suited for a systems approach to the design of life support infrastructure

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

Modeled as an inventory problem
Station nodes with quantities of passengers, vehicles
Links between connected stations with quantities of passengers & vehicles in transit
Passengers: grouped in bins by common current and final destinations
Vehicles: multiple types with different capacities, station connectivity, and operating costs

Inventory flow problem formulation:
- Conservation of passengers & vehicles moving between nodes at each time step
Passenger movement
- constrained only by vehicle capacities
- may transfer freely at any node (!)
Vehicles constrained by:
- connectivity matrix
- station / waypoint node capacity
- max fleet size limit
Arbitrary constraints somewhat easy to add:
- e.g. “max vehicles on a link segment”
- e.g. “max capacity on a group of waypoints”

Collects detailed performance metrics
- Feasibility assurance
- Continuous time execution of transit model based on integer time steps
- Inspection & analysis of track logs from individual passengers and vehicles
State persistence
- Evolve system state with all known data
- Reformulate and re-optimize schedule as scenario progresses and new input data is introduced
- Eventually allow rolling horizon scheduling
SimPy: discrete event simulation framework
LP_solve: MIP Optimization

1D Light rail scenario
- extreme linear topology
- with and without express routing (station bypass)
- 7 station nodes
2D Hexagonal network
- extreme fully-connected star topology
- with and without express routing (station bypass)
- 7 station nodes

Design Parameters
- Topology [linear 1D Rail, 2D hexagonal]
- Offline stations [sequential routing, express routing]
- Load per station [4, 64, 128, 256] commuters
- - uniform random distribution among origin stations
- Berths per station [2,4,8] vehicles
- Vehicle size [8,64,128] passengers
Assumptions
- Headways: 2 minute travel time across segments, 2 minute time to stop and transfer at a station
- Impulse demand at t = 240 min
- Vehicles must return to start configuration
- Suboptimal & nondeterministic optimization timeout at 2 hours

Dramatic improvement in mass transit performance possible by:
- Using demand-responsive routing optimization
- Constructing transfer stations off-line
We can make mass transit perform as well as personally-owned vehicles
- But this comes at a cost
- Design transit-oriented development to keep network utilization at sustainable levels
Analysts might use this tool to generate interesting data for trade studies