Advancing Urban Models in the 21 st Century. Jeff Tayman Lecturer, Dept. of Economics University of California, San Diego

Transcription

1 Advancing Urban Models in the 21 st Century Jeff Tayman Lecturer, Dept. of Economics University of California, San Diego 1

2 Regional Decision System Better tools Better data Better access Better Decisions 2

3 Structural Model Classification Economic- Demographic Urban Systems Differ in: Geographic scale Variables considered Statistical tools Policy questions Resource requirements 3

4 Census Blocks Census Tracts Community Plan Areas City Boundaries Council/Supervisorial Districts Zip Codes 33,000 MGRAs 4

5 The Parcel: The Current Frontier 5

6 Presentation Topics History of Urban System Models Model theory and structures Applications and decision making Challenges/Opportunities 6

7 History of Urban Systems Models 7

8 Urban Systems Models 1950s: Emergence Linked land use, residential and non-residential activities and the transportation system Computer capabilities Desire to use scientific methods to assess impact of highways and analyze urban problems 8

9 Urban Systems Models : 90: Maturation and Integration Despite many failed efforts, tremendous knowledge gained about urban spatial patterns Professional consolidation and attempts to integrate broad spectrum of knowledge Modeling approaches Linear statistical techniques (EMPIRIC) Linear program and optimization techniques (POLIS) Spatial interaction gravity formulations 9

10 Urban Systems Models: Widely Used Today Lee s (1973) prognosis was wrong At least 20 centers on 4 continents Most major regional planning/transportation agencies Federal legislation (ISTEA, Clean Air Acts) Policy makers under pressure to address issues related to urban form, land use, and transportation Increased computing power, data availability, and staff/consultant expertise 10

11 Urban Systems Models: Impetus for a New Generation Lack of spatial economic framework Inability to adequately answer new policy questions Excessive art and judgment Not well suited to redevelopment and reuse activities Availability of small area land economic information Enhanced computing/programming infrastructure Modeling community synergies 11

12 Model Theory and Structure: Spatial Interaction Models 12

13 Spatial Interaction Models Forecast in 5-Year 5 time intervals Links employment locations and residential locations Uses commute patterns, travel times, and land use Relies on the spatial interaction gravity model 13

14 Based on: Employment Forecast Locations of existing employment and housing Employment opportunities and attractiveness Transportation accessibility 14

15 Residential Forecast Based on: Distance from employment Residential opportunities and attractiveness Transportation accessibility 15

16 Linking the Land Use and Transportation Models Allocation Model Urban Development Model Allocation Model Employment Forecast Residential Forecast Employment Forecast Residential Forecast Land Use Characteristics Land Use Characteristics Next Increment Transportation Model Highway and Transit Transportation Model Highway and Transit 16

17 Model Theory and Structure: Production, Exchange, Consumption Allocation System (PECAS) 17

18 PECAS: Just Five Choices Where to locate? What to make and what to consume in the process (called the technology to use )? Where to buy what is consumed and where to sell what is made? What type of space (floors pace and buildings) to build? How much space to build? The interactions among these 18

19 PECAS: Components and Treatment of Time model-wide aggregate economic conditions economic changes; migration model-wide aggregate economic conditions activity allocations space development PECAS economic interactions transportation model transportation model changes in transportation year t supply year t+1 19

20 PECAS: Interactions Among Components model-wide aggregate economic conditions Economic Attractions economic changes; migration Development Activity model-wide aggregate economic conditions Activity Totals activity allocations Space Prices space development Space Quantities economic interactions Commodity Flows Generalized Transport Costs transportation model transportation model changes in transportation year t supply year t+1 20

21 Goods, Services, Labour and Space Producing Sectors Consuming Sectors $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Economic Flows $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 21

22 Economic Interactions: Production - Exchange - Consumption buying allocation process total consumption selling allocation process exchange zone exchange zone commodity flows exchange zone total production total production total production 22

23 Economic Interactions: Production - Exchange - Consumption 1: production allocation allocating production activity to zones 2: technology selection allocating production to commodities allocating consumption to commodities 3: selling allocations buying allocations 3-level nested logit model allocating produced commodities to selling locations allocating consumed commodities to buying locations 23

24 Space Development: Simulation of Transitions parcel-by-parcel microsimulation logit models industrial commercial mid density residential more the same no change derelict quantity zoning dictates set of alternatives 24

25 PECAS: Policy Analysis model-wide aggregate economic conditions economic changes; migration Economic Policy model-wide aggregate economic conditions activity allocations space development Land Use Policy Transportation Policy Land Consumption economic interactions Activity Benefits transportation model changes in transportation year t supply year t+1 transportation model Transportation Impacts 25

26 Applications and Decision Making: San Diego County 26

27 San Diego Regional Comprehensive Plan Connecting transportation and land use plans Using transportation and land use plans to guide other plans Making it happen through incentives and collaboration 27

28 SW Riverside County San Diego s s Labor Market 28

29 San Diego Daily Interaction with SW Riverside County Increased Dramatically Interregional Commuting Exchange with San Diego County Imperial Orange Riverside San Bernadino Los Angeles 29

30 Large Daily Flows Across the Border Binational Commuting Exchange with San Diego County U.S. Counties Mexico 30

31 Forecasting Modeling System Spatial Interaction Gravity Models Trip Generation Trip Distribution Mode Choice Trip Assignment 31

32 2030 Regional Futures Existing Smart Percent Policies Growth Difference PPH % Vac. Rate 3.8% 4.9% +28.9% Interregional 100,000 23, % Commute Houses 1,383,800 1,460, % Home Price ($2005) $514,000 $480, % 32

33 Future Units - Existing Policies 33

34 Future Units - Smart Growth 34

35 Existing Plan Smart Growth SF MF Vacant (SF) MF SF COMM MF OFF MF Vacant (OS) OFF COMM OS SF SF Vehicle Trips = 3,600 Vehicle Trips = 8,600 35

36 Existing Plan Smart Growth SF MF OS MF COMM School COMM SF SF MF OFF MF SF School Vehicle Trips = 32,200 Vehicle Trips = 26,700 36

37 Economic Benefits of a Highway Project 37

38 Benefits of Smart Growth Reduces sprawl and land consumption Conserves open space and habitat Reduces congestion, trip lengths, travel costs, air pollution, and interregional commuting Provides greater return on investment in transportation, especially transit 38

39 Application and Decision Making: 39

40 Sacramento Blueprint Study 40

41 Sacramento Blueprint Study 41

42 Sacramento Blueprint Study 42

43 Sacramento Blueprint Study 43

44 Sacramento Blueprint Study 44

45 Applications and Decision Making: State of Oregon 45

46 Oregon Bridge Options Study Oregon Bridge Options Study Economic Equity Impacts Broadened Policy Discussion Weight Limited Bridge Cracked Bridge State Bridges Sauvie Island Bridge Cole s Bridge McKenzie/Willamette River Bridges Local Bridges Ford s Bridge * Medium and high crack density 46

47 Oregon Bridge Options Study 47

48 Regional Production Relative to Current Mobility Option 48

49 Willamette Valley Forum Compared land use forecasts under various policies Collaborative visioning HH Growth Compared to Reference Case Many Less Than RC Same as RC Many More Than RC Clark Co. WA Clark Co. WA Clark Co. WA Clark Co. WA Portland Metro Portland Metro Portland Metro Portland Metro Salem-Keizer Salem-Keizer Salem-Keizer Salem-Keizer Corvallis Albany Corvallis Albany Corvallis Albany Corvallis Albany Eugene- Springfield Eugene- Springfield Eugene- Springfield Eugene- Springfield Picture> Picture> Picture> Picture> Highway Expansion High Speed Transit Less Land Supply VMT Tax 49

50 Challenges and Opportunities 50

51 Technical Disclosure rules Efficient management, storage, retrieval, and analysis of large, complex data Data integration More spatial and substantive detail 51

52 Integrating Information Housing Jobs Land Economics Demographics Transportation Travel Behavior Project Costs Income/Wages Performance and System Monitoring Environmental Area Boundaries 52

53 Transparency Information has the power to distort or enhance the reasoning capacity of the public Clear description of methods, data, and assumptions (avoid the black box) Publicly accessible and understandable Outcomes defensible and reasonable 53

54 The Future is Now Economic motives Agent-based modeling Developer, consumer and governmental choice Micro-simulation Synthetic populations, households,and firms 54

55 Reducing Congestion with Ridesharing Vehicle Colors SOV Carpool Bus Truck Existing Conditions Level of Service Average Speed F 19 mph 10 Mile Travel Time 31 Minutes 55

56 Reducing Congestion with Ridesharing Vehicle Colors SOV Carpool Bus Truck Existing Conditions Doubling Carpools Level of Service Average Speed F 19 mph E 38 mph 10 Mile Travel Time 31 Minutes 16 Minutes 56

57 Vehicle Colors SOV Carpool Bus Truck Reducing Congestion with Ridesharing Existing Conditions Doubling Carpools Doubling Carpools & Transit Service Level of Service F E D Average Speed 19 mph 38 mph 55 mph 10 Mile Travel Time 31 Minutes 16 Minutes 11 Minutes 57

58 Conclusions Integrated spatial economic models a reality Are being used in practical policy analysis Big job, but shrinking No longer an unknown Investment that pays off in future Essential: Iterative (Agile) development with initial model running soon The Future Continued leading edge practical studies Continued partnership with academic studies More bringing forward of experience On-going challenges with resource requirements 58

59 Advancing Urban Models in the 21 st Century Jeff Tayman Lecturer, Dept. of Economics University of California, San Diego 59