39
Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Embed Size (px)

Citation preview

Page 1: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Travel Demand Forecasting:Trip Distribution

CE331 Transportation Engineering

Page 2: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Land Use and Socio-economic Projections

Trip Generation

Trip Distribution

Modal Split

Traffic Assignment

Transportation System Specifications

Direct User Impacts

Overall Procedure

Page 3: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Trip Distribution

Where to go? Choice may vary with trip purpose

Input Trips generated from and attracted to each

zone (step 1 output) Interzonal transportation cost (travel time,

distance, out-of-pocket cost, …) Output – trip interchange between zones

Presented as Origin-Destination (OD) matrix

Page 4: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Process

Allocate trips originating from each zone to all possible destination zonesAssume destination zones are

competing with each other in attracting trips produced by zone i

Page 5: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Types of Models

Gravity ModelTrips are attracted to a zone as gravity

“attracts” objects Utility Maximization

Assumes that a traveler makes the decision that maximizes his/her utility

Page 6: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Calculating TAZ “Attractiveness”

Page 7: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Gravity Model

Page 8: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering
Page 9: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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)

Page 10: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Gravity Model Example Problem

Page 11: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Input data

How do models compute this? See next pages…

Does this table need to be

symmetrical? Is it usually?

Page 12: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Convert Travel Times into Friction Factors

Yes, but how

did we get

these?

Page 13: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 14: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 15: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 16: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 17: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 18: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 19: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 20: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 21: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 22: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 23: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 24: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 25: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 26: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 27: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 28: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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

Page 29: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Calculate the Attractiveness of Each Zone

Page 30: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Calculate the Relative Attractiveness of Each

Zone

Make sense

?

Page 31: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Distribute Productions to TAZs

Page 32: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

First Iteration Distribution

Advanced Concepts – not required for CE331

Page 33: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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)

Advanced Concepts – not required for CE331

Page 34: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

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 exerciseAdvanced Concepts – not required for CE331

Page 35: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

A Simple Gravity Model

tij = Pi Tj/(dijn Aj)

Where

Pi – trips generated from zone i;

dij – distance or time;

Tj – trips attracted to zone j;

Aj – balancing factor;

Aj =Σ (Pi /dijn)

n – parameter.

Page 36: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Example – 1

A new theater is expected to attract 700 trips from 2 zones with daily trip productions of 1500 and 3000. The distances to the new theater are 2 and 3 miles, respectively. The factor n is approximately 2. How many trips from each zone will be attracted to the theatre?

Page 37: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Example (cont’d)

A1 = Σ (Pi /dijn) = (1500/22)+ (3000/32)=

708.3

t11=P1T1/(d112A1)=1500(700)/[22(708.3)]=

370.6

t21=P2T1/(d212A1)=3000(700)/

[32(708.3)]=329.4

Page 38: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Utility Maximization

Consider travelers’ choice in trip-making decision

Use utility function (U) to reflect the attractiveness of an alternative (in this case, destination)U = b0 + b1*z1 + b2*z2 + … + bn*zn

• b0, b1, …: parameters

• z1, z2, …: attributes of the alternative

Page 39: Travel Demand Forecasting: Trip Distribution CE331 Transportation Engineering

Utility Maximization:Logit Model

j

U

U

j

i

e

ei)Pr(

Pr(i): probability of choosing alternative i over all other alternatives;

Ui: utility value of alternative (destination) i.