China Planning Network Urban Transportation Roundtable June 18

China Planning Network: Urban Transportation Roundtable June 18, 2009 Beijing Using Urban Information Systems to Relate Travel Demand to Urban Form Prof. Joseph Ferreira, Jr. Head, Urban Information Systems MIT Urban Studies & Planning Department

Talk Outline l Challenge: managing metro growth l l Opportunity: new tools for ‘responsive’ cities l l l Urbanization & motorization pressure Unsustainable energy and green-house gas implications Growth management especially important in China Measure (in)efficiency of metro growth Take advantage of new spatial data infrastructure Example: Metro-Boston growth management l l Quantify impact of growth management scenarios l Utilize administrative data l Be sensitive to neighborhood scale Evaluate benefits of efficient urban form and cost of environmental externalities CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 2

Challenge: Managing Metro Growth l Urbanization & motorization pressure l l Rapid motorization: 1000 new cars per day in Beijing No end in sight l l l Millions yet to come to cities Wrong price incentives and weak political will Unsustainable energy and green-house gas implications l l Inefficient urban form and travel patterns due to unpriced externalities Significant global impact CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 3

Opportunity: New Tools for ‘Responsive’ Cities l Measure (in)efficiency of metro growth l l l Address unpriced externalities Make cities ‘responsive’ not rigidly regulated Take advantage of new spatial data infrastructure l Administrative data now includes location l l l Build urban models from administrative data l l GIS data layers: roads, buildings, property boundaries… Transaction info: Transit fare cards, congestion charges, cellphone use… Avoid need for expensive surveys Enable improved monitoring and discrete choice modeling Accelerate responsiveness of urban planning Requires new tools and safeguards l l Next-generation urban models (distributed, modular, activity-based) Locational privacy and anonymized data CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 4

Example: Metro-Boston Growth Management l Illustrate ideas with straightforward example l l Quantify impact of growth management scenarios l l l Class projects work with State and Metro planning agencies Examine spatial relationships: travel, urban form, and demographics Extrapolate travel impacts of alternative growth scenarios Utilize administrative data and official plans l l l Land use, transportation infrastructure, demographics Urban activity and travel behavior Metro growth scenarios and new integrated data layers CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 5

Metro-Boston Regional Planning Key Agencies l State Agencies l EOEEA: Exec. Office of Energy & Environmental Affairs l l l Mass. GIS State GIS agency: ‘Climate Roadmap’ effort: CO 2 consequences of urban growth Registry of Motor Vehicles l l Metropolitan Area Planning Council (MAPC) l Regional planning agency l l Vehicles registration, licensing, and safety inspection records part of Boston Metro Planning Organization (MPO) 101 member Municipalities (out of 164 in metro region) Metro. Future - ongoing regional planning for 2030 Central Transportation Planning Staff (CTPS) l Transportation planning part of Boston MPO l l Transportation and policy analysis support for Metro Boston Journey to work and travel time data by traffic analysis zone (TAZ) CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 6

Metro Boston 164 municipalities (with local land use control) United States Massachusetts Boston Metro CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 7

Metro. Future Growth Areas, 2000 -2030 CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 8

Class Project Examples l Collaborate with MAPC and Mass. GIS l l Estimate l l Spring 2008 and 2009 class projects (1/2 semester) VMT (vehicle miles traveled) implications of MAPC growth scenarios How much extra travel if Boston growth is ‘compact’ vs. ‘sprawl’ VMT relationship to urban form, neighborhood demographics, and accessibility Illustrate use of improved GIS and administrative data l l Detailed population and business locations Vehicle miles traveled (VMT) from safety inspections CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 9

Class projects with MAPC and Mass. GIS Half-semester projects in Advanced GIS class Spring 2008 project: l l l Estimate non-work travel implications of Alternative Metro. Future Growth Scenarios Ph. D and MCP students on project: Wanli Fang, Paul Green, Lissa Harris, Shan Jiang, Masayoshi Oka, Abner Oliveira, Yi Zhu Project co-supervision with Dr. Fabio Carrera Spring 2009 project: l l l Examine spatial pattern of VMT across Boston Ph. D and MCP students on project: Andrew Gulbrandson, Casey Hunter, Yang Jiang, Jae Seung Lee, Jingsi Xu, Lulu Xue, and Jiyang Zhang Lab assistance from Mi Diao (advanced Ph. D student) Most maps shown below are from student work CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 10

Metro. Future Growth Areas, 2000 -2030 CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 11

Source: MAPC model and MIT 11. 521 Project Work (Spring 2008) Metro. Future Forecast for New Housing Demand 2005 - 2030 (by TAZ) ‘Winds of Change’ Scenario ‘Let It Be’ Scenario +348505 CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 +307476 12

New Grid Layer from Mass. GIS l l Integrate spatially detailed data about l Residences, jobs, businesses l Geography, land use and infrastructure l Private passenger VMT by place of residence Onto new 250 x 250 meter grid cell raster l l 125 K cells for metro Boston Overlay GIS layers to allocate census population, businesses, employment, and VMT to grid cells CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 13

Mass GIS Allocation of Population to 250 x 260 m Grid Cells …near 2 nd ring road, 40 km from Boston CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 14

Business Locations …with Euclidean Distance from Grocery Stores to Grid Cells CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 15

New VMT Estimates from Safety Inspection Data l Mandatory annual safety inspection l l l All private passenger autos (5 million in state) Odometer mileage reported to RMV since 2001 Estimate annual mileage (VMT) of every vehicle l l l Geocode every auto to place of residence Estimate annual mileage from 2+ inspections Allocate mileage to 250 x 250 m grid cell CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 16

Geocode Vehicles to Residential address and allocate annual mileage to grid cell Sources: • Mass. GIS estimates of annual mileage developed from Registry of Motor Vehicles safety inspection and vehicle registration data • Maps developed by MIT 11. 521 class project (Spring 2009) CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 17

Spring 2008 Class Project Use new Data to Analyze Metro. Future scenarios l Estimate non-work VMT patterns for current households l l Get housing growth locations from Metro. Future scenarios l l l Residences from Census and land use data Destinations from job and business location data Non-work travel from proximity calculations Let-It-Be (LIB) scenario - business as usual Wind-of-Change (WOC) scenario - corridor and activity center emphasis Further details l l Assume new growth has VMT behavior of current residents Account for 16 types of housing + development constraints, etc. CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 18

Source: MAPC model and MIT 11. 521 Project Work (Spring 2008) New Demand for Housing by 2030 (by TAZ) l ‘Winds of Change’ Scenario l ‘Let It Be’ Scenario +348505 CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 +307476 19

Source: MIT 11. 521 Project Work (Spring 2008) In-Accessibility of non-Work Destinations • Each cell in the grid has a value representative of the total meters traveled oneway for an “average” (nonwork) trip for a single household. • Non-Work VMT = Average Non work trip distance (tripmerge) * No of households (hshlds_250 m) * 4. 18 trips/household CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 20

Source: MIT 11. 521 Project Work (Spring 2008) Visualization of Accessibility to Non-Work Destinations Darker colors have higher accessibility (with closer non-work destinations) CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 21

Scenario Differences in Estimated VMT Scenario Allocation Average Non-Work VMT/day per new HH Percent Increase Winds of Change Low VMT within TAZ 1. 85 km baseline Let It Be Low VMT within TAZ 2. 51 km +36 % Random within TAZ 2. 80 km +51 % CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 22

Lessons from Spring 2008 Class Project l l Location of growth can have big impact on non-work travel Scenario analysis is plausible as two month project l l If administrative data, derived GIS layers, and MAPC scenarios are in place Results are easily explained and accepted CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 23

Spring 2009 Class Project l Next step - look at total travel impact of growth scenarios l l Examine actual VMT for every private passenger vehicle in the state for 20052008 l l Not just non-work travel 6+ million records from safety inspection data Visualize and explain VMT variations across metro area CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 24

Geocode Vehicles to Residential address and allocate annual mileage to grid cell Sources: • Mass. GIS estimates of annual mileage developed from Registry of Motor Vehicles safety inspection and vehicle registration data • Maps developed by MIT 11. 521 class project (Spring 2009) CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 25

Spatial Pattern in Annual Vehicle Miles Traveled (VMT) by 250 x 250 m grid cell …per Vehicle or …per Household Red = low VMT Blue = high VMT Sources: MIT 11. 521 project analysis of Mass. GIS and RMV data CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 26

Annual VMT per Vehicle MIT 11. 521 class project work Spring 2009 Annual VMT per Household Annual VMT by Location (250 x 250 m grid cell) CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 27

Demographics Indicators for Metro-Boston Census Block Groups Thematic maps of: • Household % with kids • Household income • Moved in 45 years (%) • Tenure (% that rent) • etc…. MIT 11. 521 Urban Transport Roundtable - Joe Ferreira - June 18, 2009 CPN 09: class project Spring 2009 28

Combine Demographic Indices using Factor Analysis into 3 Primary Factors: Factor 1: Income and Vehicles Factor 2: Non-standard Families Factor 3: Number of Workers CPN 09: Urban Transport Roundtable 2009 MIT 11. 521 class project Spring - Joe Ferreira - June 18, 2009 29

Accessibility Indicators Example: Number of jobs accessible in 30 minutes (Shaded TAZ are within 30 minutes by auto from central location near MIT) CPN 09: Urban Transport Roundtable 2009 MIT 11. 521 class project Spring - Joe Ferreira - June 18, 2009 30

Land Use and Urban Form Measures …Use Factor Analysis to build urban form indicators CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 MIT 11. 521 class project Spring 2009 31

Build VMT Model from Factors VMT [for each 250 x 250 m grid cell] = Function of § § § Demographic factors Accessibility factors Urban Form factors Simple weighted linear regression explains almost 50% of grid cell variation in actual annual mileage. CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 32

Conclusions from Spring 2009 Project l Big differences between city and suburb l l Interesting Income effects l l l Twice the total annual mileage (per household) in the suburbs Higher income lower VMT per vehicles (but more vehicles) Higher income much higher VMT per household Significant demographics, accessibility and urban form effects l l l Explain 50% of VMT variation across 250 x 250 m grid cells Non-work accessibility matters Additional neighborhood variations l l l Less VMT along transportation corridors & activity centers Less VMT in mixed use areas Next step – repeat LIB and WOC comparison l estimate regional benefit of compact development in total VMT CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 33

General Conclusions l New Tools for ‘Responsive’ Cities l l Make cities ‘responsive’ not rigidly regulated Modern urban information infrastructure can l l l Scenario analysis is plausible as two month project l l Accelerate pace of evaluation and response: Enable self-governance through better measurement of urban form externalities If administrative data, derived GIS layers, and MAPC scenarios are in place Results are easily explained and accepted CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 34

General Conclusions (Part 2) Take advantage of new spatial data infrastructure l Administrative data now includes location l l l Build urban models from administrative data l l GIS data layers: roads, buildings, property boundaries… Transaction info: Transit fare cards, congestion charges, cellphone use… Avoid need for expensive surveys Enable improved monitoring and discrete choice modeling Accelerate responsiveness of urban planning Invest in new tools and safeguards l l Next-generation urban models (distributed, modular, activity-based) Locational privacy and anonymized data CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 35

Additional Links l MIT Department of Urban Studies & Planning l l Department of Urban Studies & Planning (DUSP): http: //dusp. mit. edu Urban Information Systems (UIS): http: //mit. edu/dusp/uis l Mass. GIS: http: //www. mass. gov/mgis MAPC: http: //mapc. org l Related GIS infrastructure sites: l l l Open Geospatial Consortium (OGC): http: //www. opengeospatial. org Urban and Regional Information Systems Association (URISA): http: //www. urisa. org Thank you – Joe Ferreira, jf@mit. edu CPN 09: Urban Transport Roundtable - Joe Ferreira - June 18, 2009 谢谢！ 36