beyond business as usual: ensuring the network we want is the network we get

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Beyond Business as Usual: Ensuring the Network We Want is the Network We Get David Levinson, David Levinson, Norah Montes de Oca and Norah Montes de Oca and Feng Xie Feng Xie Mn/DOT Final TAP Meeting May 30th, 2006 RESULTS

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Beyond Business as Usual: Ensuring the Network We Want is the Network We Get. RESULTS. David Levinson, Norah Montes de Oca and Feng Xie. Mn/DOT Final TAP Meeting May 30th, 2006. Project Outline. Interviews Develop flowcharts Develop IF … THEN RULES Develop models - PowerPoint PPT Presentation

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Page 1: Beyond Business as Usual:  Ensuring the Network We Want is the Network We Get

Beyond Business as Usual: Ensuring the Network We Want is the Network We Get

David Levinson, David Levinson,

Norah Montes de Oca andNorah Montes de Oca and

Feng XieFeng Xie

Mn/DOT Final TAP Meeting

May 30th, 2006

RESULTS

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Project OutlineProject Outline

• Interviews Interviews • Develop flowcharts Develop flowcharts •Develop IF…THEN RULES• Develop models• Update simulation program data• Incorporate all models• Compare different scenarios• Analyze results • Make conclusions and suggestions

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General Framework QuickTime™ and a

TIFF (LZW) decompressorare needed to see this picture.

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Network t of links thathave not been investedin since the base year

Pool t oflegacy links

Rank of candidateprojects for year t

Saving ofstate budgetup to year t

State budgetestimated for year t

State budgetavailable for year t

State budgetsufficient?

Estimate themaintenance cost

V/C ratio abovethreshold?

State scoring

Estimate the numberof lanes added

Estimate theexpansion cost

Projectranked k

k=1

Candidateremains?

Save remaining moneyfor next year

No

Yes

No

Yes

Project approved;budget allocated

k= k+1

No

Yes

The State Level

The County Level

TIME

Year t Yeart+1

The Flowchart of the Investment Model of SONG2.0

Investment Model

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Models used:

Trips Population

Retail Employment

Non Retail Employment

Residential Density

Distance Distance Sq

Trips 1.00 Population 0.95 1.00 Retail Employment 0.11 0.01 1.00 Non Retail Employment -0.00 -0.11 0.37 1.00 Residential Density -0.03 -.04 0.03 0.17 1.00 Distance -0.14 -.12 -0.18 -0.25 -0.08 1.00 Distance Sq -0.15 -0.10 -0.16 -0.19 -0.05 0.94 1.00

Trip Generation Regression model - trips produced

Regression Statistics

Multiple R = 0.9654 R Square = 0.9320 Adjusted R Square = 0.9316 Standard Error = 49.5971 Observations = 1165

Coef. Std. Err. t Stat P-value Intercept Population Retail Non Retail Res Density Distance Distance Sq

-14.8999 0.1083 0.0348 0.0110 -0.0003 0.0052 -1.2008

4.8897 0.0009 0.0044 0.0010 0.0060 0.0004 9.1483

-3.0472 123.5499 7.8301 11.3189 -0.0534 12.3988 -13.1258

0.0023 0 1.095E-14 3.029E-28 0.9574 3.077E-33 8.597E-37

.Trip Generation Regression model - trips attracted

Regression Statistics

Multiple R = 0.8152 R Square = 0.6646 Adjusted R Square = 0.6629 Standard Error = 190.7532 Observations = 1165

Coef. Std. Err. t Stat P-value Intercept Population Retail Non Retail Res Density Distance Distance Sq

50.8388 0.0209 0.1977 0.1380 -0.0862 0.0027 -9.326E-08

18.8059 0.0033 0.0171 0.0037 0.0231 0.0016 3.518E-08

2.7033 6.2057 11.5633 36.8559 -3.7371 1.7000 -2.6508

0.0070 7.5671E-10 2.4093E-29 2.207E-197 0.0002 0.0894 0.0081

Trips Population

Retail Employment

Non Retail Employment

Residential Density

Distance Distance Sq

Trips 1.00 Population 0.03 1.00 Retail Employment 0.48 0.01 1.00 Non Retail Employment 0.77 -0.11 0.37 1.00 Residential Density 0.06 -.04 0.03 0.17 1.00 Distance -0.27 -.12 -0.18 -0.25 -0.08 1.00 Distance Sq -0.23 -0.10 -0.16 -0.19 -0.05 0.94 1.00

Correlation results:

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.State highway budget model

Regression Statistics

Multiple R = 0.9072 R Square = 0.8230 Adjusted R Square = 0.8176 Standard Error = 15451478.8 Observations = 35

Coef. Std. Err. t Stat P-value Intercept Total VMT

22146206.1 0.01012081

3986497.14 0.00081703

5.55530465 12.3873636

3.5803E-06 5.8937E-14

Correlation results

Expenditures Total VMT Expenditures 1.00 Total VMT 0.91 1.00

County highway budget model

Regression Statistics

Multiple R = 0.9574 R Square = 0.9166 Adjusted R Square = 0.8976 Standard Error = 4920 Observations = 28

Coef. Std. Err. t Stat P-value [95% Conf-Interval] Intercept Households Total VMT Dummy for 1990 Dummy for 1995 Dummy for 2000

10289970.76 -78.0026 0.0078 -11552700.9 -5900211.09 -3491842.33

2546068.811 39.3296 0.0019 3169562.041 2903788.393 2541778.176

4.0415 -1.9833 4.1598 -3.6449 -2.0319 -1.3218

0.0005 0.0599 0.0004 0.0014 0.0544 0.1998

5009741.56/15570200 -159.567/3.5621 0.0039/0.0117 -18125977.3/-4979424.47 -11922306.1/121883.918 -8970560.82/1986876.16

County Budget – Correlation results

Expenditures Households Total VMT Expenditures 1.00 Households 0.79 1.00 Total VMT 0.88 0.98 1.00

Models used: (continuation)

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Calibration

RMSE= 33%Average peak hour difference = 0.78%

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Constraints

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Legacy Links

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Scenario Expansion Decision Rules

New Construction Decision Rules

Total Budget

Expansion Construction Budget Split

New Link Choice Set

1 Stated Revealed Standard 50/50 Legacy 2 Most

structured Revealed Standard 50/50 Legacy

3 Least structured

Revealed Standard 50/50 Legacy

4 Stated Revealed Scenarios 50/50 Legacy 5 Stated Revealed Standard 25/75 Legacy 6 Revealed Revealed Standard 50/50 Legacy 7 Revealed Revealed Standard 50/50 All

potential

Scenarios Tested

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Scenario 1.

Baseline - State

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Scenario 2. Most

Structured - State

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Scenario 3.

Least Structured

- State

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Scenario 4.

Budget Increase400% - State

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Scenario 5.

Budget split25-75%State

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Scenario 6. LK

(legacy links) Model - State

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Scenario 7. LK (new

choice set)

Model - State

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Scenario 1.

Baseline -

Counties

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Scenario 2. Most

Structured - Counties

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Scenario 3. Least

Structured - Counties

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Scenario 4.

Budget Increase400% -

Counties

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Scenario 5.

Budget split25-75%

Counties

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Scenario 6. LK

(legacy links) Model - Counties

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Scenario 7. LK (new

choice set)

Model - Counties

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New Link Construction

Counties - New Link Construction

0

20

40

60

80

100

120

2005 2010 2015 2020 2025 2030 Year

Lane kilometers

Scenario 1. Baseline Scenario 3. Least Structured Scenario 4c. Increase400

Scenario 4e. Decrease25 Scenario 6. LK-Expansion Scenario 7. LK-New Construction

State - New Link Construction

0

20

40

60

80

100

120

140

160

180

2005 2010 2015 2020 2025 2030 Year

Lane kilometers

Scenario 1. Baseline Scenario 3. Least Structured Scenario 4c. Increase400

Scenario 4e. Decrease25 Scenario 6. LK-Expansion Scenario 7. LK-New Construction

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Link Expansion

Counties - Link Expansion

0

50

100

150

200

250

300

350

2005 2010 2015 2020 2025 2030Year

Lane kilometers

Scenario 1. Baseline Scenario 3. Least Structured Scenario 4c. Increase400

Scenario 4e. Decrease25 Scenario 6. LK-Expansion Scenario 7. LK-New Construction

State - Link Expansion

0.00

100.00

200.00

300.00

400.00

500.00

600.00

2005 2010 2015 2020 2025 2030 Year

Lane kilometer

Scenario 1. Baseline Scenario 3. Least Structured Scenario 4c. Increase400

Scenario 4e. Decrease25 Scenario 6. LK-Expansion Scenario 7. LK-New Construction

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MOE: AccessibilityMeasure of Effectiveness : Accessibility

-40,000

-30,000

-20,000

-10,000

0

10,000

20,000

30,000

2005 2010 2015 2020 2025 2030

in Millions

Years

Difference in values

Scenario 2 - Scenario 1 Scenario 3 - Scenario 1 Scenario 4c - Scenario 1Scenario 5 - Scenario 1 Scenario 6 - Scenario 1 Scenario 7 - Scenario 1

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MOE:Trip timeMeasure of Effectiveness : Trip time

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

2005 2010 2015 2020 2025 2030

Year

Differences in minutes

Scenario 2 - Scenario 1 Scenario 3 - Scenario 1 Scenario 4c - Scenario 1

Scenario 5 - Scenario 1 Scenario 6 - Scenario 1 Scenario 7 - Scenario 1

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MOE: Trip lengthMeasure of Effectiveness : Trip length

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

2005 2010 2015 2020 2025 2030

Year

Differences in kilometers

Scenario 2 - Scenario 1 Scenario 3 - Scenario 1 Scenario 4c - Scenario 1

Scenario 5 - Scenario 1 Scenario 6 - Scenario 1 Scenario 7 - Scenario 1

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MOE: VKTMeasure of Effectiveness : Vehicle Kilometer Travel

-300

-200

-100

0

100

200

300

400

2005 2010 2015 2020 2025 2030

in Millions

Year

Differences

Scenario 2 - Scenario 1 Scenario 3 - Scenario 1 Scenario 4c - Scenario 1

Scenario 5 - Scenario 1 Scenario 6 - Scenario 1 Scenario 7 - Scenario 1

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MOE: VHT

Measure of Effectiveness : Vehicle Hour Travel

-400

-300

-200

-100

0

100

200

300

400

500

2005 2010 2015 2020 2025 2030

in Millions

Year

Difererences

Scenario 2 - Scenario 1 Scenario 3 - Scenario 1 Scenario 4c - Scenario 1

Scenario 5 - Scenario 1 Scenario 6 - Scenario 1 Scenario 7 - Scenario 1

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Conclusions

• As budget rises:• vkt rises• Network expands• Accessibilityincreases• Users spend less time on the road

• vht declines

Future Work:• Reduce Step length -

five year model to one year model

Reasons:• To test an

evolutionary model of network growth

• Only a percentage of work trips would change destinations

• in a given year• Finer level of

resolution

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Questions ?

Comments ?

Thank you