ideas + action for a better city...2017/02/28 · analytical modeling at the metropolitan...
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Ideas + Action for a Better Citylearn more at SPUR.org
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Analytical Modeling at the Metropolitan Transportation Commission
February 28, 2017
Lisa Zorn ([email protected]) & Michael Reilly ([email protected])
Overview• Models in regional planning• Mike on UrbanSim, the land use model• Lisa on Travel Model One, the transportation model• Questions
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Please note• Today’s talk is not a formal presentation in the Plan
Bay Area 2040 process• Scenarios are earlier versions
• Any comments will not be part of the EIR process• Please see http://planbayarea.org if you would like
to learn more or participate
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What are Regional Models?• Complex, data-hungry computer programs• Use economics and statistics to forecast how
different parts of the city work and interact in an attempt to forecast the future
• At MTC, they use microsimulation• Explicit prediction of choices (e.g., Where do I
want to live? What time will I drive home?)5
Why Use Regional Models?• Forecast the future to better understand trajectory
and plan/evaluate transportation investments• Rigorous, consistent, and comprehensive
• Test the efficacy of transport and land use policies• Better understand how the region works and
what might ameliorate our problems• Evaluating alternate futures or scenarios
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Regional Models at MTC• Various software forms an integrated model
• Regionwide total forecast: REMI• Local land use forecast: UrbanSim• Transportation behavior: CT-RAMP• Emissions: EMFAC• Other: health, benefit-c0st, equity assessment
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Land Use Modeling• UrbanSim land use model
• Developed by Paul Waddell, UCB• Forecasts the intra-regional location
of households and jobs (and thebuildings that contain them) for a series of future years
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Supply in UrbanSim• Start with map of all current buildings
• Attributes such as size, age, price• All households and jobs are explicitly assigned
using recent data on their locations
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Demand in UrbanSim• Statistical equations developed from past behavior
explain the consistencies in location preferences• Every year new households (from REMI) and some
existing households choose a new housing unit• Very individual but there are correlations • Place them in these locations
• Jobs are similar12
Increasing Supply• Map of land use policies
• Mostly zoning, but also caps, fees, subsidies
• UrbanSim Developer Model simulates construction• Pro forma estimates profit = revenue – costs
• Costs from existing use, fees, constuction• Revenue starts with current prices, goes up in areas of
high demand
• Build the most profitable buildings13
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Mini Pro Forma
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Costs –$98m
Revenue $162m
Profit $64m
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Scenarios• Built multiple scenarios with stakeholders
• Different visions relating to where growth ought to go• Use policies within the model to achieve
• Different transportation investments and policies
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Four Initial PBA Scenarios• No Project and
PBA40 Scenarios Visions
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Transportation ModelingTravel Model 1 forecasts the travel behavior of every resident on a typical weekday in the future
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Demand in TM 1• Travel is a derived demand
• Start with land use model output: where do people live and where are their destinations?
• Explicit representation of people in households making many interrelated choices• Car ownership, where working, shopping, when
leave for a trip, what mode (car, walk, transit…)26
Supply in TM 1• Detailed representation of the travel network
• Roads with capacity, tolls• Transit with frequency, costs
• How do the trips generated by the demand model combine throughout the day to generate congestion and affect travel speeds/times
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Scenario Output• Back to the scenarios introduced earlier• Travel Model used to assess alternate futures
• Different land use patterns means a different set of origins and destinations for trips
• Vary transportation investments and policies• Assess which one (or combinations) best achieve
regional goals30
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Figure 2: Change in Roadw ay Lane Miles from 20 10
San
FranciscoSan Mateo Santa Clara Alam eda Contra Costa Solano Napa Sonom a Marin
-1.0 %
0 .0 %
1.0 %
2.0 %
3.0 %
4.0 %
5.0 %
6.0 %
7.0 %
8.0 %
9.0 %
10 .0 %
11.0 %
12.0 %
Gro
3313
Figure 3: Change in Transit Passenger Seat Miles from Year 20 10
Transit Technology
Local Bus Light Rail Ferry Express Bus Heavy Rail Com m uter Rail
0 %
5%
10 %
15%
20 %
25%
30 %
35%
40 %
45%
50 %
55%
60 %
Ch
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Figure 9: Year 20 35 Autom obile Ow nership Results
Zero autom obiles One autom obile Tw o autom obiles Three autom obilesFour or m ore
autom obiles
0 %
2%
4%
6%
8%
10 %
12%
14%
16%
18%
20 %
22%
24%
26%
28%
30 %
32%
34%
36%
38%
40 %
Sh
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Figure 12: Year 20 35 Autom obile Mode Shares for All Travel
Single occupant, No HOT
Single occupant, Pay touse HOT Tw o occupants, No HOT
Tw o occupants, Payto use HOT Three or m ore occupants
0 %
5%
10 %
15%
20 %
25%
30 %
35%
40 %
45%
50 %
55%
Sha
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Figure 14: Year 20 35 Typical W eekday Transit Boardings by Technology
Local Bus Light Rail Ferry Express Bus Heavy Rail Com m uter Rail
0
10 0 ,0 0 0
20 0 ,0 0 0
30 0 ,0 0 0
40 0 ,0 0 0
50 0 ,0 0 0
60 0 ,0 0 0
70 0 ,0 0 0
80 0 ,0 0 0
90 0 ,0 0 0
1,0 0 0 ,0 0 0
1,10 0 ,0 0 0
1,20 0 ,0 0 0
1,30 0 ,0 0 0
1,40 0 ,0 0 0
1,50 0 ,0 0 0
1,60 0 ,0 0 0
1,70 0 ,0 0 0
1,80 0 ,0 0 0
Typ
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Figure 15: Year 20 35 Vehicle Miles Traveled per Hour by Tim e Period
Early AM (3 am to 6 am ) AM Peak (6 am to 10 am ) Midday (10 am to 3 pm ) PM Peak (3 pm to 7 pm ) Evening (7 pm to 3 am )
0 M
1M
2M
3M
4M
5M
6M
7M
8M
9M
10 M
11M
12M
13M
14M
VM
38
39
41
42
43
25%
75%
24%
33%
43%
23%
77%
21%
33%
46%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
outside PDA
in PDA
Inland, Coastal,
Delta
Bayside
Big 3 Cities
Compared to the Draft Preferred Scenario, the Final Preferred
Scenario boosts housing growth in the “Big 3” cities.
Where will the region plan for the 820,000new households?
30%
40%
30%
2010: 2.6 millionhouseholds
34%
38%
28%
2040: 3.4 million households
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Draft
Draft
Draft
Draft
Draft
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48%
52%
14%
46%
40%
45%
55%
17%
40%
43%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
outside PDA
in PDA
Inland, Coastal,
Delta
Bayside
Big 3 Cities
New strategies included in the Final Preferred Scenario shifted
some job growth away from Bayside communities.
Where will the region plan for the 1.3 millionnew jobs?
33%
41%
26%
36%
41%
23%
2010: 3.4 million jobs
2040: 4.7 million jobs
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Draft
Draft
Draft
Draft
Draft
Ideas + Action for a Better Citylearn more at SPUR.org
tweet about this event:
@SPUR_Urbanist
#MTCModeling