sprawl dynamics
DESCRIPTION
Sprawl Dynamics. Prof. Philip C. Emmi College of Architecture + Planning University of Utah RailVolution 2005. SimTropolis. Dynamic yet Radically Simplified Metro-Regional Scale Integrated Urban Land Use & Transportation Scenario Generator For the Exploration of Alternative Urban Futures. - PowerPoint PPT PresentationTRANSCRIPT
Sprawl Dynamics
Prof. Philip C. EmmiCollege of Architecture +
PlanningUniversity of Utah
RailVolution 2005
SimTropolis
• Dynamic yet Radically Simplified• Metro-Regional Scale• Integrated Urban Land Use &
Transportation Scenario Generator• For the Exploration of Alternative
Urban Futures
Self-Reinforcing Relationship between
Urban Land Development Urban Road Building
Sprawl FeedbackSprawl Feedback
As Urban Land Develops, Traffic and As Urban Land Develops, Traffic and Vehicle Miles Traveled IncreasesVehicle Miles Traveled Increases
1
Increased Road Capacity Increased Road Capacity Induces Intensity IncreasesInduces Intensity Increases
3
At Higher Intensities, More Land is At Higher Intensities, More Land is Needed for People and JobsNeeded for People and Jobs
4
Increased Travel Demand Increased Travel Demand Leads to More RoadsLeads to More Roads
2
Study Area and Time Horizon
• Urbanized Portions of the Salt Lake, Davis and Weber Counties
• Initialized with 1980 data• Model calibration data: 1980 - 2000• Validation experiments
– Calibration: 1980 - 1992.– Project and compare with 2002
observations
• Projection and policy horizon: 2030
URBANLAND
ROADMILES
developingAcres
road building
InitialUrban Land
InitialRoadMi
Ac\Person
PctDensityIncrease
Vehicle Travel
TargetRoadMiles
DesiredRoadMiles\DVMT
PctTripsNotSingleOccupancy
NonAutoTrafficEquiv
RoadGap
RoadBuildingImplement'nLag
PostPolicyDVMTs
SmoothDensityChange
NonAutoSmthStep
ImplementLag2
Chng InAc\Person
BetterCapacityFromExistingRoads
SmoothCapacityChange
ImplementLag1
PEOPLE
Chng InPeople
Developing Land & Building Roads
System EquationsDVMTs X 10^3 = f(Urban Acres)
y = 0.1221*Acres - 3979
R2 = 0.999
14000
16000
18000
20000
22000
24000
26000
28000
30000
140000 160000 180000 200000 220000 240000 260000 280000
Acres/Capita = f(Road MIles)
Acres/Capita = 0.00001435 * RoadMiles + 0.1316R2 = 0.9975
0.165
0.17
0.175
0.18
0.185
0.19
0.195
0.2
0.205
2500 3000 3500 4000 4500 5000
DVMTs = 0.12214 * (URBAN_LAND) – 3979
Road Miles at t+5 per DVMT*10^3 at t)
RoadMiles(t) / DVMT(t+5) = -0.00133* Year + 2.858
0.195
0.2
0.205
0.21
0.215
0.22
0.225
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998
DesiredRoadMiles\DVMT = -0.00133 * TIME + 2.858
Ac\Person = 0.00001435 * ROAD_MILES + 0.1316
TargetRoadMiles = DesiredRoadMiles\DVMT * DVMTs
RoadGap = (TargetRoadMiles - ROAD_MILES)
road_building = (RoadGap / RoadBuilding_Implement'nLag)
developingAcres = (PEOPLE * Chng_In_Ac\Person) +
(Ac\Person * Chng_In_People)
Percent Change in Urban Performance Indicators, 1980 - 2000
Test Experiment #1• Divide the 1980 - 2002 data set into
2 halves• Calibrate the model on data from
1980-1992• Project to 2002, compare with 2002
observations and note the errors• Find the RMS error of the projections
for:– Urban Land, VMTs, Road Miles & Density
• RMS Error is found to be low
Test Experiment #2
• Note slight logarithmic structure to data on Ac\Person as a function of Road Miles.
• Fit a logarithmic function to the data. • Use it in place of a linear function.• Note effects on year 2030 simulations.
– In the baseline scenario, values are 10-15% lower.
– In other scenarios, values are 1 - 3% lower.
Test # 3 (Incomplete)• Note that there is little agreement among
alternative data series on urban land.• Would the model behave differently if another
data series on urban land were used?• Would the model behave differently if it were
calibrated on the CO Front Range?• Does the model’s data determine its
characteristic behavior or its structure?• Does the model’s data or its structure
determine the (in)accuracy of its historical simulations?
Percent Change in Urban Performance Indicators, 1980 - 2030
Four Alternative Future Scenarios
1. Baseline: an extension of historic practice2. Land Use:
Increase new development densities by 20%
3. Transportation: Reduce single-occupancy vehicle trips to 80% of totalIncrease existing road capacity by 12%Speed up TIP implementation from 5 to 3 years
4. Combine Land use & Transportation
Urban Land Consumption:Four Scenarios
Daily Vehicle Miles Traveled
Road Miles
Intensity of Land Use
The Density of New Development
Acres Developed Per Year
Crude Road Congestion Index
Extensions
•Agricultural land preservation•Water use and conservation•Urban forest and canopy cover•Vehicle fuel and energy use•Carbon dioxide emissions•Criteria air pollutants
Conclusions about Urban Dynamic System
Models• They are simple to understand• They are highly accurate• They are easy to manipulate• They illustrate dynamic complexity• They generate alternative policy
scenarios• They facilitate learning about policy
options• They might facilitate policy negotiation