Ideas + Action for a Better City learn more at SPUR.org. tweet about this #MTCModeling

Transcription

1 Ideas + Action for a Better City learn more at SPUR.org tweet about this #MTCModeling

2 Analytical Modeling at the Metropolitan Transportation Commission February 28, 2017 Lisa Zorn & Michael Reilly

3 Overview Models in regional planning Mike on UrbanSim, the land use model Lisa on Travel Model One, the transportation model Questions 3

4 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 if you would like to learn more or participate 4

5 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

6 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 6

7 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 7

8 Land Use Modeling UrbanSim land use model Developed by Paul Waddell, UCB Forecasts the intra-regional location of households and jobs (and the buildings that contain them) for a series of future years 8

9 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 9

10 10

11 11

12 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 similar 12

13 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 buildings 13

14 14

15 15

16 16

17 17

18 Mini Pro Forma Costs $98m Revenue $162m Profit $64m 18

19 19

20 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 20

21 Four Initial PBA Scenarios No Project and 21

22 PBA40 Scenarios Visions 22

23 23

24 24

25 Transportation Modeling Travel Model 1 forecasts the travel behavior of every resident on a typical weekday in the future 25

26 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

27 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 27

28 28

29 29

30 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 goals 30

31 31

32 Gro San Francisco San Mateo Santa Clara Alam eda Contra Costa Solano Napa Sonom a Marin 12.0 % 11.0 % 10.0 % 9.0 % 8.0 % 7.0 % 6.0 % 5.0 % 4.0 % 3.0 % 2.0 % 1.0 % 0.0 % -1.0 % Figure 2: Change in Roadw ay Lane Miles from

33 Ch Transit Technology Local Bus Light Rail Ferry Express Bus Heavy Rail Commuter Rail 60 % 55% 50 % 45% 40 % 35% 30 % 25% 20 % 15% 10 % 5% 0 % Figure 3: Change in Transit Passenger Seat Miles from Year

34 Sh 40 % Zero autom obiles One autom obile Tw o autom obiles Three autom obiles Four or m ore autom obiles 38% 36% 34% 32% 30 % 28% 26% 24% 22% 20 % 18% 16% 14% 12% 10 % 8% 6% 4% 2% 0 % Figure 9: Year Autom obile Ow nership Results 34

35 Sha 55% Single occupant, No HOT Single occupant, Pay to use HOT Tw o occupants, No HOT Tw o occupants, Pay to use HOT Three or m ore occupants 50 % 45% 40 % 35% 30 % 25% 20 % 15% 10 % 5% 0 % Figure 12: Year Autom obile Mode Shares for All Travel 34 35

36 Typ Local Bus Light Rail Ferry Express Bus Heavy Rail Commuter Rail 1,80 0, ,70 0, ,60 0, ,50 0, ,40 0, ,30 0, ,20 0, ,10 0, ,0 0 0, , , , , , , , , , Figure 14: Year Typical W eekday Transit Boardings by Technology 37 36

37 VM 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 ) 14M 13M 12M 11M 10 M 9M 8M 7M 6M 5M 4M 3M 2M 1M 0 M Figure 15: Year Vehicle Miles Traveled per Hour by Tim e Period 37

38 38

39 39

40 Questions? 40

41 41

42 42

43 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 820,000 new households? Big 3 Cities 43% Draft 30% 30% Bayside Inland, Coastal, Delta in PDA outside PDA 24% 25% 33% 21% Draft 23% Draft 33% Draft 46% 75% 77% Draft 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2010: 2.6 million households 28% 40% 2040: 3.4 million households 38% 34% 8 43

44 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 million new jobs? Big 3 Cities Bayside Inland, Coastal, Delta in PDA outside PDA 14% 40% 46% 17% Draft 52% 48% 43% Draft 40% Draft 45% 55% Draft Draft 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2010: 3.4 million jobs 26% 23% 41% 41% 33% 2040: 4.7 million jobs 36% 9 44

45 Ideas + Action for a Better City learn more at SPUR.org tweet about this #MTCModeling