Travel Implications of MetroFuture Growth Scenarios

1. 11.521/11.524 Spatial Database Management and Advanced Geographic Information Systems (GIS) Travel Implications of MetroFuture Growth Scenarios Jie Xia (MCP1), Jingsi Xu (MCP2) Prof. Joseph Jr. Ferreira 05/13/2010

2. Outline • Vision for MetroFuture Plan • Methodology in Improving Annual Vehicle Miles of Travel (VMT) Database • Travel Implications of Current Trends Scenario at Regional Level • Travel Implications of MetroFuture Plan at Local Level

3. Vision for MetroFuture Plan-I • Link transportation planning with land-use and economic-development plans, particularly in areas identified for development by state, regional, and local planning.

4. Metro Boston Community Types

5. Vision for MetroFuture Plan-II • Put priority on existing centers of economic activity; or to areas with adequate transportation infrastructure; or to municipal centers or areas targeted for economic development. (CODAs=1*) * CODAs: Community Oriented Development Areas

6. Metro Boston Community Oriented Development Areas (CODAs)

7. Analysis Databases • New Households Allocation under Three Scenarios (WOC, LIB and LIB-random) from TAZs to Grid Cells (11.521 08’) • Vehicles Miles of Travel (VMT) Database from MAPC (11.521 09’) • Demographic Data (250*250m) at grid cell from MassGIS • 2000 Census Data at block-group level

8. Different Levels of Spatial-Analysis Units • Town: 164 • TAZ: 2727 • Block Group: 3320 • Grid Cell: 119332

9. Key Factors in Projecting the Increase of Vehicle Miles of Travel (VMT) Total VMT=(VMT/VIN)*(VINs/HH)*(HHs) • Vehicle miles of travel per vehicle (VMT/VIN) • Vehicles per household (VINs/HH) • Spatial differences • “Inner Core” to “Developing Suburbs” • Socio-economic differences • Housing Types • Household Income • Household Size • Etc.

10. VMT per Vehicle Estimation • VMT Estimation Method 1) Excluding outliers in the annual VMT dataset • Low end: if VMT<1,000 then VMT=1,000 • High end: if VMT>30,000 then VMT=30,000 2) Estimating VMT per vehicle for each cell • ‘Good’ cells: no less than 12 vehicles within a cell • Simple average • ‘Bad’ cells: less than 12 vehicles within a cell • IDW (inverse distance weighted); power=2

11. Framingham ‘good’ cell • G250M_ID: 173790 • Number of Vehicles: 196 ‘bad’ cell • G250M_ID: 174632 • Number of Vehicles: 4

12. Metro Boston: Annual VMT per vehicle is 11,716 miles

13. Vehicles per Household Estimation- I • Step 1. Identifying cells having reasonable counts of households and vehicles ‘good’ cell = simple averaging value (9-cell catchment: >40 households & >60 vehicles & VIN/HH>0 & VIN/HH<5) ‘bad’ cell = block-group level averaging value • Question: “How to combine two datasets with different spatial-statistical scales?”

14. Vehicles per Household Estimation- II Step 2.Summing up the numbers of households and vehicles in the nearest 9 cells and calculating the ratio of VINs per household ‘good’ cell • G250M_ID: 173790 • Number of Households: 386 • Number of Vehicles: 684 ‘bad’ cell • G250M_ID: 175468 • Number of Households: 15 • Number of Vehicles: 30 ‘good’ cell • G250M_ID: 174632 • Number of Households: 423 • Number of Vehicles: 566

15. Vehicles per Household Estimation- II • Step 3: Exaggerating the ratios of ‘good’ cells by 5% • Step 4: For ‘bad’ cells, using block-group level average (VIN/HH=H046001/H044001 ) • Step 5: Second round of averaging the ratios of VIN/HH in the 9-cell spatial catchment * *: H044001: Total occupied housing units H046001: Aggregate number of vehicles available

16. Metro Boston: Average vehicles per household is 1.54

17. Statistical Results for VMT Analysis • Currently, in Metro Boston area • 1.6 million households • 2.5 million vehicles • 29 billion of annual miles of driving (VMT) Table 1. VMT Data Analysis for Different Community Types

18. Difference of VMT between CODA and Non-CODA TAZs Table 2. Comparison of VMT in Different Types of TAZs

19. Travel Implications of MetroFuture at Local Level?