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

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

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.

Metro Boston Community Types

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

Metro Boston Community Oriented Development Areas (CODAs)

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

Different Levels of Spatial-Analysis Units Town: 164  TAZ: 2727  Block Group: 3320 Grid Cell:

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.

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

Framingham ‘good’ cell G250M_ID: Number of Vehicles: 196 ‘bad’ cell G250M_ID: Number of Vehicles: 4

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

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?”

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

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/H ) 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

Metro Boston: Average vehicles per household is 1.54

Statistical Results for VMT Analysis Number of Households Number of Vehicles Total VMT (Unit: miles) VMT/VINVIN/HHVMT/HH Inner Core544,194556,2075,604,056,79910, ,298 Regional Urban Centers 400,839585,4266,806,777,66211, ,981 Maturing Suburbs 359,623683,8938,046,223,29111, ,374 Developing Suburbs 300,200645,3638,492,177,40213, ,288 Total1,604,8562,470,88928,949,235,15411, ,039 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

Difference of VMT between CODA and Non-CODA TAZs CODAS OTHER TAZS VMT per vehicle (unit: miles) 11,00212,724 Vehicles per household VMT per household (unit: miles) 14,13127,225 Table 2. Comparison of VMT in Different Types of TAZs

Travel Implications of MetroFuture at Local Level?