Travel Implications of MetroFuture Growth Scenarios Jie Xia (MCP1), Jingsi Xu (MCP2) Prof. Joseph...
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Transcript of Travel Implications of MetroFuture Growth Scenarios Jie Xia (MCP1), Jingsi Xu (MCP2) Prof. Joseph...
![Page 1: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/1.jpg)
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)
![Page 2: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/2.jpg)
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
![Page 3: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/3.jpg)
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.
![Page 4: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/4.jpg)
Metro Boston
Community Types
![Page 5: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/5.jpg)
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
![Page 6: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/6.jpg)
Metro Boston
Community Oriented
Development Areas
(CODAs)
![Page 7: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/7.jpg)
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
![Page 8: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/8.jpg)
Different Levels of Spatial-Analysis Units Town: 164
TAZ: 2727 Block Group: 3320
Grid Cell: 119332
![Page 9: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/9.jpg)
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.
![Page 10: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/10.jpg)
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
![Page 11: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/11.jpg)
Framingham
‘good’ cell G250M_ID: 173790 Number of
Vehicles:
196
‘bad’ cell G250M_ID: 174632 Number of
Vehicles: 4
![Page 12: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/12.jpg)
Metro Boston:
Annual VMT
per vehicle is
11,716 miles
![Page 13: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/13.jpg)
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?”
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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
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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
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Metro Boston:
Average vehicles
per household is
1.54
![Page 17: 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.](https://reader036.fdocuments.net/reader036/viewer/2022070415/5697bfe91a28abf838cb6854/html5/thumbnails/17.jpg)
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
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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
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Travel Implications of MetroFuture at Local Level?