source: nhi course on travel demand forecasting, ch. 6 (152054a) trip distribution
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Source: NHI course on Travel Demand Forecasting, Ch. 6 (152054A)
Trip Distribution
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
Terminology
• Friction factor• Gravity model• K-factors• Trip Distribution
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
Calculating TAZ “Attractiveness”
Gravity Model
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)
Example Problem
Input data
How do models compute this? See next pages…
Does this table need to be
symmetrical? Is it usually?
Convert Travel Times into Friction Factors
Yes, but how
did we get
these?
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
…
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Calculate the Attractiveness of Each Zone
Calculate the Relative Attractiveness of Each
Zone
Make sense
?
Distribute Productions to TAZs
First Iteration Distribution
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)
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
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