Travel Implications of MetroFuture Growth Scenarios

Jie Xia (MCP1), Jingsi Xu (MCP2)

Prof. Joseph Jr. Ferreira

05/13/2010

11.521/11.524 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 (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

Different Levels of Spatial-Analysis Units Town: 164

TAZ: 2727 Block Group: 3320

Grid Cell: 119332

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 Method1) 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: 173790 Number of

Vehicles:

196

‘bad’ cell G250M_ID: 174632 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: 173790 Number of

Households: 386 Number of Vehicles: 684

‘good’ cell G250M_ID: 174632 Number of

Households: 423 Number of Vehicles:

566

‘bad’ cell G250M_ID: 175468 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/H044001 )

Step 5: Second round of averaging the ratios of VIN/HH in the 9-cell spatial catchment

Legend

vehicles per household

0.00

- 1.

50

1.51

- 1.

82

1.83

- 2.

01

2.02

- 2.

17

2.18

- 4.

02

*

*: 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/VIN VIN/HH VMT/HH

Inner Core 544,194 556,207 5,604,056,799 10,075 1.02 10,298

Regional Urban

Centers400,839 585,426 6,806,777,662 11,627 1.46 16,981

Maturing Suburbs 359,623 683,893 8,046,223,291 11,765 1.90 22,374

Developing Suburbs

300,200 645,363 8,492,177,402 13,159 2.15 28,288

Total 1,604,856 2,470,889 28,949,235,154 11,716 1.54 18,039

• 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

CODASOTHER TAZS

VMT per vehicle

(unit: miles)11,002 12,724

Vehicles per household

1.28 2.14

VMT per household

(unit: miles)14,131 27,225

Table 2. Comparison of VMT in Different Types of TAZs

Travel Implications of MetroFuture at Local Level?