Area Type Sub Model Estimation Area Type Sub Model Estimation AT - - PowerPoint PPT Presentation

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Area Type Sub Model Estimation Area Type Sub Model Estimation AT - - PowerPoint PPT Presentation

Feb 20, 2024 30 likes •242 views

Area Type Sub Model Estimation Area Type Sub Model Estimation AT classification used in: AT classification used in: -Estimating highway capacities -Estimating highway capacities -Stratifying work trip attractions -Stratifying work

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Area Type Sub Model Estimation Area Type Sub Model Estimation

  • AT classification used in:

AT classification used in:

  • Estimating highway capacities
  • Estimating highway capacities
  • Stratifying work trip attractions
  • Stratifying work trip attractions
  • Summarize results of mode choice model
  • Summarize results of mode choice model
  • Why Modeling AT?

Why Modeling AT?

  • Should be sensitive with the changes in future data
  • Should be sensitive with the changes in future data
  • Simple definitions not possible using individual zonal data

Simple definitions not possible using individual zonal data

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Model Estimation Methodology Model Estimation Methodology

  • “Model team” prepared a “Subjective” map

“Model team” prepared a “Subjective” map

  • Used as the “Target Map”

Used as the “Target Map”

  • Area Types based on geography and local knowledge

Area Types based on geography and local knowledge

  • Rural

Rural

  • Urban
  • Urban
  • CBD
  • CBD
  • Assumptions in Model Estimation

Assumptions in Model Estimation

  • AT of a TAZ depends on the PD and ED and/or LOS

AT of a TAZ depends on the PD and ED and/or LOS

  • AT of a TAZ is related to surrounding zones
  • AT of a TAZ is related to surrounding zones
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SLIDE 4

4 8 12 Miles

Map layers

TAZs without Lakes

AT1

1 2 3

“Subjective” AT Map

Winston-Salem Greensboro High Point Burlington

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SLIDE 5

TAZs plotted by pop and emp density

0.10 1.00 10.00 100.00 1000.00 10000.00 100000.00 1000000.00 1.00 10.00 100.00 1000.0 10000. 00 10000 0.00 PD ED Rural Urban CBD

Original TAZ distributions

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SLIDE 6

Model Estimation Methodology Contd. Model Estimation Methodology Contd.

  • Need for considering the surrounding

Need for considering the surrounding zones zones

  • Which Surrounding zones need to be

Which Surrounding zones need to be considered? considered?

  • 3 Approaches Tested

3 Approaches Tested

  • Zones within a distance specified by

Zones within a distance specified by

  • User (X)
  • User (X)
  • A multiple (F) of Zonal Units (ZU),
  • A multiple (F) of Zonal Units (ZU),

Where ZU = SQRT(A) Where ZU = SQRT(A) and and

  • Adjacent Zones (Physically touching)
  • Adjacent Zones (Physically touching)

640 725 454 626 636 634 530 623 523 633 789 526 638 791 632 723

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SLIDE 7

Model Estimation Methodology Contd. Model Estimation Methodology Contd.

  • Which approach gives best results?

Which approach gives best results?

  • Approach 1: X is varied from 0.5 to 2.5 mi. @ 0.5 interval.

Approach 1: X is varied from 0.5 to 2.5 mi. @ 0.5 interval.

  • Approach 2: F is varied from 0.75 to 2 @ 0.25 interval
  • Approach 2: F is varied from 0.75 to 2 @ 0.25 interval
  • Approach 3: No variation
  • Approach 3: No variation
  • TAZs

TAZs distributed graphically by W/ distributed graphically by W/Avg

  • Avg. of PD and ED & observed

. of PD and ED & observed in GIS in GIS

  • Better distribution of area types

Better distribution of area types

  • Difficult to define AT
  • Difficult to define AT
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SLIDE 8

Observed AT classification from Approach 2, F=1.5

Observed Area Type Classification

0.10 1.00 10.00 100.00 1000.00 10000.00 100000.00 1.00 10.00 100.00 1000.00 10000.00 PD ED Rural Urban CBD

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SLIDE 9

Model Estimation Methodology Contd. Model Estimation Methodology Contd.

  • Need of a statistical analysis

Need of a statistical analysis

  • Discriminant

Discriminant Classification Test: Classification Test:

  • Target classes : Existing AT classes from “Subjective” map

Target classes : Existing AT classes from “Subjective” map Rural =1, Urban = 2, CBD=3 Rural =1, Urban = 2, CBD=3

  • Variables used : PD and ED
  • Variables used : PD and ED
  • Results:
  • Results:

– – Classification function coefficients Classification function coefficients – – Classification table Classification table – – Case wise representation of observed and predicted AT Case wise representation of observed and predicted AT

  • Classification tables compared for each approach

Classification tables compared for each approach

  • Approach 2 with F = 1.5 is selected to be the best for Triad

Approach 2 with F = 1.5 is selected to be the best for Triad

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0.718 0.718 Overall Correct Class. Rate Overall Correct Class. Rate 0.513 0.513 20 20 4 4 15 15 3 3 0.599 0.599 17 17 664 664 428 428 2 2 0.996 0.996 2 2 505 505 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table

Table 1: AT classification using PD and ED of individual TAZs Table 2: AT classification using Approach 1, X = 0.5

0.746 0.746 Overall Correct Class. Rate Overall Correct Class. Rate 0.795 0.795 31 31 8 8 3 3 0.630 0.630 35 35 699 699 375 375 2 2 0.994 0.994 3 3 504 504 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table

Table 3: AT classification using Approach 1, X = 1

0.743 0.743 Overall Correct Class. Rate Overall Correct Class. Rate 0.846 0.846 33 33 6 6 3 3 0.632 0.632 74 74 701 701 334 334 2 2 0.978 0.978 11 11 496 496 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table

Table 4: AT classification using Approach 1, X = 1.5

0.732 0.732 Overall Correct Class. Rate Overall Correct Class. Rate 0.846 0.846 33 33 6 6 3 3 0.613 0.613 116 116 680 680 313 313 2 2 0.982 0.982 9 9 498 498 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table

Discriminant Classification Results

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Table 5: AT classification using Approach 1, X = 2 0.711 0.711 Overall Correct Class. Rate Overall Correct Class. Rate 0.846 0.846 33 33 6 6 3 3 0.587 0.587 157 157 651 651 301 301 2 2 0.970 0.970 15 15 492 492 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table Table 6: AT classification using Approach 1, X = 2.5 0.687 0.687 Overall Correct Class. Rate Overall Correct Class. Rate 0.846 0.846 33 33 6 6 3 3 0.558 0.558 194 194 619 619 296 296 2 2 0.957 0.957 22 22 485 485 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table Table 7: AT classification using Approach 2, F =0.75 0.721 0.721 Overall Correct Class. Rate Overall Correct Class. Rate 0.538 0.538 21 21 6 6 12 12 3 3 0.604 0.604 17 17 670 670 422 422 2 2 0.992 0.992 1 1 3 3 503 503 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table Table 8: AT classification using Approach 2, F = 1.00 0.737 0.737 Overall Correct Class. Rate Overall Correct Class. Rate 0.615 0.615 24 24 10 10 5 5 3 3 0.628 0.628 19 19 697 697 393 393 2 2 0.984 0.984 8 8 499 499 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table

Discriminant Classification Results Contd.

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SLIDE 12

Table 9: AT classification using Approach 2, F = 1.25 0.764 0.764 Overall Correct Class. Rate Overall Correct Class. Rate 0.718 0.718 28 28 9 9 2 2 3 3 0.662 0.662 15 15 734 734 360 360 2 2 0.990 0.990 5 5 502 502 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table Table 10: AT classification using Approach 2, F =1.5 0.764 0.764 Overall Correct Class. Rate Overall Correct Class. Rate 0.769 0.769 30 30 9 9 3 3 0.663 0.663 13 13 735 735 361 361 2 2 0.986 0.986 7 7 500 500 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table Table 11: AT classification using Approach 2, F = 2 0.762 0.762 Overall Correct Class. Rate Overall Correct Class. Rate 0.744 0.744 29 29 10 10 3 3 0.661 0.661 14 14 733 733 362 362 2 2 0.984 0.984 8 8 499 499 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table Table 12: AT classification using Approach 3 0.735 0.735 Overall Correct Class. Rate Overall Correct Class. Rate 0.641 0.641 25 25 13 13 1 1 3 3 0.625 0.625 12 12 693 693 404 404 2 2 0.984 0.984 8 8 499 499 1 1 Classified Classified 3 3 2 2 1 1

  • Act. Group
  • Act. Group

Correctly Correctly Pred

  • Pred. Group

. Group Classification Table Classification Table

Discriminant Classification Results Contd.

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SLIDE 13

Predicted AT Classification by Discriminant Analysis on Approach 2, F=1.5

Predicted Area Type Classification 0.10 1.00 10.00 100.00 1000.00 10000.00 100000.00 1.00 10.00 100.00 1000.00 10000.00 PD ED Rural Urban CBD

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4 8 12 Miles

AT-150

1 2 3

Difference between Predicted AT Map and Target Map

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Figure 6: AT Classification using Temporary “Suburban” Category.

0.10 1.00 10.00 100.00 1000.00 10000.00 100000.00 1.00 10.00 100.00 1000.00 10000.00 Rural Suburban Urban CBD

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5 10 15 Miles

ATFINAL

Rural Suburban Urban CBD

Predicted AT Map using Temporary Suburban Class

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Final Adjustments Final Adjustments

  • CBD

CBD

  • CBD

CBD TAZs TAZs not properly not properly separated from Urban separated from Urban

  • Some
  • Some TAZs

TAZs outside the target

  • utside the target

CBD have high ED CBD have high ED

  • Not contiguous
  • Not contiguous
  • Network density is used as an

Network density is used as an additional variable additional variable

  • Outlying Zones

Outlying Zones

  • If 70% or more zones
  • If 70% or more zones

surrounding a TAZ are of surrounding a TAZ are of different AT different AT

ED >= 10,000 and Hwy Network ED >= 10,000 and Hwy Network Density>=12 Density>=12 CBD CBD <>rural and <>CBD <>rural and <>CBD Urb Urb PD (between 0 and 500) and PD (between 0 and 500) and ED (between 0 and 1000) ED (between 0 and 1000) Rural Rural Criteria Criteria AT AT

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4 8 12 Miles

AT-02-SMOOTH

Rural (523) Urban (1100) CBD (32)

Base Year (2002) AT Classification

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4 8 12 Miles

AT-15-SMOOTH

1.00 (397) 2.00 (1223) 3.00 (35)

2015 AT Classification

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4 8 12 Miles

AT-25-SMOOTH

1.00 (310) 2.00 (1307) 3.00 (38)

2025 AT Classification

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4 8 12 Miles

AT-35-SMOOTH

1.00 (238) 2.00 (1370) 3.00 (47)

2035 AT Classification