source: nhi course on travel demand forecasting, ch. 6 (152054a) trip distribution
TRANSCRIPT
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Source: NHI course on Travel Demand Forecasting, Ch. 6 (152054A)
Trip Distribution
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Objectives
• Describe inputs and outputs to gravity model
• Explain concept of friction factors• Explain how friction factors are
obtained• Apply gravity model to sample
data set
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Terminology
• Friction factor• Gravity model• K-factors• Trip Distribution
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Key concepts
• Trip distribution is a method to determine where trips are going from and to
• Trip interchange, or OD• “match up” the
productions and attractions• Calibrate to reflect current
travel patterns• Apply (aka evaluate) to
forecast future travel patterns
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Calculating TAZ “Attractiveness”
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Gravity Model
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K-Factors
• K-factors account for socioeconomic linkages not accounted for by the gravity model
• Common application is for blue-collar workers living near white collar jobs (can you think of another way to do it?)
• K-factors are i-j TAZ specific (but could use a lookup table – how?)
• If i-j pair has too many trips, use K-factor less than 1.0 (& visa-versa)
• Once calibrated, keep constant? for forecast (any problems here???)
• Use dumb K-factors sparingly• Can you design a “smart” k factor? (TTYP)
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Example Problem
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Input data
How do models compute this? See next pages…
Does this table need to be
symmetrical? Is it usually?
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Convert Travel Times into Friction Factors
Yes, but how
did we get
these?
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
Find the shortest path from node to all other nodes (from Garber and Hoel)1
Yellow numbers represent link travel times in minutes3
Here’s how
…
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 11
2
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 21
2
4
5
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 31
2
4
5
4
4
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 41
2
4
4
Eliminate
5 >= 4
4
5
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 51
2
4
4
4 10
6
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1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 61
2
4
4
4 10
6
7Eliminate
7 >= 6
7
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6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 71
2
4
4
4 10
6
Eliminate8 >= 7
8
7
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7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 81
2
4
4
4 10
7
6
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10
7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 91
2
4
4
4 10
7
6
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10
7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 101
2
4
4
4 10
7
6
10Eliminate
10 >= 7
10
Eliminate
10 >= 10
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7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 111
2
4
4
4 10
7
6
10
10
8
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7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 121
2
4
4
4 10
7
6
10
8
9
910
Eliminate 10 > 9
Eliminate
10 >= 9
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7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 131
2
4
4
4
7
6
10
8
9
9
12 12
Eliminate
12 >= 10
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7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
631
232
334
444
132 1
132 1
242 1
STEP 141
2
4
4
4
7
6
10
8
9
9
12 10
Eliminate
12 >= 10
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7
8
6
1 2 3 4
5 6 7 8
9 10 11 12
14 15 1613
FINAL1
2
4
4
4
7
6
10
8
9
9
10
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Calculate the Attractiveness of Each Zone
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Calculate the Relative Attractiveness of Each
Zone
Make sense
?
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Distribute Productions to TAZs
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First Iteration Distribution
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Comparing and Adjusting Zonal Attractions
• Balanced attractions from trip generation = 76
• The gravity model estimated more attractions to TAZ 3 than estimated by the trip generation model.
• What can we do? (see homework)
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Forecasting for Future Year Assignments
• After successful base year calibration and validation (review … how?)
• Use forecast land use, socioeconomic data, system changes
• Forecasted production and attractions, and future year travel time skims
• Apply gravity model to forecast year• Friction factors remain constant over
time (what to you think?)
In-class exercise