1 modeling the behavioral determinants of travel behavior: an application of latent transition...

1

Modeling the behavioral determinants of travel behavior: an application of

latent transition analysis

Maarten KroesenSection Transport and Logistics (TLO)

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Transportation: a mixed blessing

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Sustainable mobility

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• What determines people’s mode choice? (cost, travel time, flexibility, income, attitudes)

• What are the patterns of substitution / complementarity between modes?

If better PT generates travel (instead of substituting car travel) better PT is of little use…

Two types of questions

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What are the patterns of substitution / complementarity between modes?Golob, T. F. and Meurs, H. (1987) A structural model of temporal change in multi-modal travel demand. Transportation Research Part A: Policy and Practice, 21, 391-400.

Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e

6


Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e

Insights:• Travel behavior is generally inert

++

++

++

7


Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e

Insights:• Travel behavior is generally inert• Car demand is affected by bicycle

demand, but not by PT demand

++

++

++

-

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Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e


demand, but not by PT demand• Bicycle demand is affected by car

and PT demand

++

++

++

-

-

-

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Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e


demand, but not by PT demand• Bicycle demand is affected by car

and PT demand• PT demand is not affected by car or

bicycle demand

++

++

++

-

-

-

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Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e

Questions:• No direct effect between PT and car

use, but maybe cycling aids in the transition from car to PT?

• Which kind of transitions can be identified?

• Which travel patterns can be identified?

• What is the influence of external conditions/events on transition behavior (e.g. sex, age, moving house)?

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++

++

-

-

-

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An alternative conceptualization

Travel behavio

r pattern

s

Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e

e

e

e

Travel behavio

r pattern

s

SexAge

Moved house

Year 2

Year 1 A B CTravel pattern A Paa Pab PacTravel pattern B Pba Pbb PbcTravel pattern C Pca Pcb Pcc

Matrix of transition probabilities

LCM LCM

Latent transition model

Data

• The Dutch mobility panel • 10 bi-annual waves (March and September)

from 1984 to 1989• 3500-4000 individuals per wave• Analysis was based on 6 March waves• Data were pooled into 2 waves• N=5,314

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Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

x1 x2 x3 x4

y1 y2 y3

z1 z2 z3 z4

Wave 1 Wave 2

x1 x2

y1 y2

z2 z3

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Descriptivestatistics

Variable Wave 1 Wave 2Weekly trips by car Mean (SD) 7.2 (9.5) 7.1 (9.3)Weekly trips by bicycle Mean (SD) 7.5 (8.6) 7.0 (8.4)Weekly trips by public transport

Mean (SD) 1.4 (3.4) 1.3 (3.2)

Sex (%)Male 50 50Female 50 50

Age Mean (SD)37.3

(17.1)38.3

(17.1)

Education level (%)High school / vocational education

79 78

Higher education 20 21

Income (%)

0 - 15,000 guilders 53 4915,000 - 34,000 guilders 37 37>34,000 guilders 5 7Missing 6 7

Occupational status (%)

Works in government 12 13Works in company or self-employed

29 30

Student 22 21Works in household 22 21Retiree 7 8Other 8 8Missing 0 0

City type (%)Small city 76 77Big city (Amsterdam or Rotterdam)

24 23

Car license holder (%)No 40 38Yes 60 62

Number of cars in household (%)

0 20 201 66 652 or more 14 16

Train season-ticket holder (%)No 96 97Yes 4 3

Moved house (%)No 87 Yes 13

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Distributions (N=5,314)

Trips by car Trips by bicycle Trips by PT

Count variables integer and positive

Latent class model

Travel behavio

r pattern

s

Car use

Bicycle use

Public transport

use

Year1

e

e

e

LCM

Count data assume that LC represents a mixture of Poisson distributions (i.e. each class is associated with a different Poisson mean for each indicator),

such that the associations between the residuals equal 0 (assumption of conditional independence, similar to FA)

0

0

0

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Finding the optimal number of classes Bivariate residuals

N=5,314Number

of classes

LL L² df p-value%

Reduction in L2 (H0)

car-bicycl

e

car-public

bicycle-public

Wave 1

1-

8735112356

3 5311 0.00 0.007919.

7 2587.8 125.1

2-

58949 66757 5300 0.00 0.46 14.8 10.4 1061.4

3-

49385 47629 5289 0.00 0.61 48.7 0.0 68.2

4-

44381 37621 5278 0.00 0.70 0.1 1.7 17.4

5-

41069 30998 5267 0.00 0.75 2.1 1.6 1.5

6-

39637 28135 5256 0.00 0.77 1.2 0.0 1.2

7-

38417 25693 5245 0.00 0.79 3.8 41.8 1.5

8-

37515 23891 5234 0.00 0.81 7.6 37.5 0.2

9-

36738 22336 5223 0.00 0.82 6.4 0.1 0.1

10-

36024 20909 5212 0.00 0.83 0.7 0.1 0.6

Wave 2

1-

8554712049

3 5311 0.00 0.007676.

2 2679.8 63.1

2-

57616 64631 5300 0.00 0.46 3.9 16.4 958.6

3-

48310 46018 5289 0.00 0.62 15.1 0.5 45.3

4-

43811 37020 5278 0.00 0.69 2.3 5.2 0.7

5-

40457 30313 5267 0.00 0.75 0.1 1.6 3.3

6-

39127 27653 5256 0.00 0.77 1.1 0.3 12.9

7-

37974 25346 5245 0.00 0.79 1.7 13.0 11.2

8-

36985 23368 5234 0.00 0.81 0.0 9.8 1.1

9-

36266 21930 5223 0.00 0.82 0.1 0.6 1.5

10-

35564 20527 5212 0.00 0.83 3.3 0.2 1.2

<3.84 n.s.

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N=5,314 Class 1 2 3 4 5Class size (%) 28 28 18 16 11

Indicators

Car trip rate Poisson mean 0.2 18.6 0.9 11.1 1.0

Bicycle trip rate Poisson mean 17.1 0.6 1.3 11.2 5.3

Public transport trip rate

Poisson mean 0.6 0.3 0.5 0.3 9.5

5-class solution: indicator profiles

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Latent class model with covariates

Travel behavio

r pattern

s

Car use

Bicycle use

Public transport

use

Year1

e

e

e

SexAge

Moved house

LCM

Multinomial logit (MNL) model

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N=5,314 (wave 1) Class 1 2 3 4 5Class size (%) 28 28 18 16 11

Indicators

Car trip rate Mean 0.2 18.6 0.9 11.1 1.0

Bicycle trip rate Mean 17.1 0.6 1.3 11.2 5.3

Public transport trip rate

Mean 0.6 0.3 0.5 0.3 9.5

Active covariates

Sex (%)Male 44 69 30 56 47Female 56 31 70 44 53

Age Mean 27.6 40.8 46.5 39.4 34.6

Moved house (%)No 87 87 89 86 89Yes 13 13 11 15 11

Inactive covariates

Education level (%)High school / vocational education

84 74 89 69 78

Higher education 15 25 9 31 22

Income (%)

0 - 15,000 guilders 76 24 66 42 5915,000 - 34,000 guilders 16 60 26 48 31>34,000 guilders 1 11 2 6 4Missing 8 5 7 3 6

Occupational status (%)

Works in government 7 18 5 21 12Works in company or self-employed

14 50 16 35 22

Student 53 3 10 6 39Works in household 18 12 44 25 13Retiree 3 8 13 6 8Other 5 9 12 6 6

Community type (%)Small city 79 80 71 83 59Big city (Amsterdam or Rotterdam)

21 21 29 17 41

Car license (%)No 76 2 57 4 69Yes 24 98 43 96 31

Number of cars in household (%)

0 32 1 28 4 481 58 72 62 86 452 or more 10 27 10 10 7

Train season ticket (%)

No 98 99 97 99 79Yes 2 1 3 1 21

Latent classprofiles

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Latent transition model with covariates

Travel behavio

r pattern

s

Car use

Bicycle use

Public transport

use

Car use

Bicycle use

Public transport

use

Year1 Year2

e

e

e

e

e

e

Travel behavio

r pattern

s

SexAge

Moved house

MNL model

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Wave 2

Wave 1 Parameter SB SC LT JCB PT (ref.)

Intercept-2.16 (-

6.03)-0.96 (-

3.27)-1.98 (-

7.11)-2.88 (-

5.26)0

Strict bicycle user

Slope4.60

(10.12)1.33 (2.01) 2.94 (4.79) 4.79 (6.35) 0

Sex (female)-0.31 (-

1.33)-1.72 (-

3.58)0.01 (0.02)

-0.23 (-0.66)

0

Age (standardized) 0.23 (1.17)-0.42 (-

0.42)0.88 (4.23)

-0.14 (-0.31)

0

Age2 0.22 (1.24)-1.66 (-

2.16)-0.03 (-

0.13)-1.30 (-

3.13)0

Moved house (yes) 0.05 (0.16) 1.23 (2.51) 0.46 (1.13) 0.51 (1.17) 0

Strict car user

Slope 1.59 (3.28)4.69

(10.57)2.95 (5.17) 4.77 (7.22) 0

Sex (female) 0.93 (1.39) 0.16 (0.34) 1.21 (2.39) 0.77 (1.54) 0

Age (standardized)-0.21 (-

0.34)0.78 (2.08) 1.13 (2.64) 0.64 (1.62) 0

Age2 0.22 (0.35)-0.17 (-

0.38)-0.15 (-

0.32)-0.08 (-

0.17)0

Moved house (yes) 0.03 (0.04)-0.49 (-

1.00)-0.86 (-

1.44)-0.12 (-

0.23)0

Light traveler

Slope 2.58 (5.82) 3.02 (6.56) 4.48 (8.41) 3.70 (5.18) 0

Sex (female) 0.15 (0.33)-1.16 (-

2.50)0.00 (0.00)

-0.63 (-1.14)

0


0.70)0.64 (3.04) 1.07 (5.32) 0.97 (3.17) 0

Age2 0.10 (0.59)-0.47 (-

2.69)-0.22 (-

1.50)-1.30 (-

2.28)0

Moved house (yes) 0.19 (0.32) 0.57 (0.92)-0.04 (-

0.07)-0.86 (-

0.93)0

Joint car and bicycle user

Slope 3.74 (5.59) 4.24 (6.35) 4.26 (5.25) 7.74 (9.75) 0

Sex (female) 0.04 (0.07)-0.84 (-

1.37)0.19 (0.28)

-0.63 (-1.06)

0

Age (standardized) 0.88 (1.62) 1.30 (2.63) 2.01 (3.24) 1.57 (3.26) 0

Age2 -0.78 (-1.36)

-1.15 (-2.90)

-0.97 (-2.68)

-1.15 (-3.22)

0

Moved house (yes)-0.94 (-

1.32)-0.86 (-

1.23)-0.95 (-

1.12)-0.99 (-

1.47)0

Public transport user

Slope (ref.) 0 0 0 0 0

Sex (female) 0.10 (0.35)-0.76 (-

1.99)0.60 (1.79) 0.64 (1.03) 0


3.50)0.08 (0.28) 0.26 (1.68)

-0.67 (-1.09)

0

Age2 0.05 (0.29)-0.98 (-

2.80)-0.11 (-

0.84)-1.22 (-

2.38)0

Moved house (yes) 0.93 (2.43)-0.76 (-

0.97)0.24 (0.48) 0.73 (1.00) 0

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(N=5,314) Wave 2Wave 1 SB SC LT JCB PTStrict bicycle user 0.75 0.03 0.09 0.06 0.07Strict car user 0.02 0.78 0.09 0.10 0.02Light traveler 0.14 0.12 0.65 0.05 0.05Joint car and bicycle user 0.11 0.20 0.06 0.62 0.02

Public transport user 0.13 0.07 0.12 0.03 0.65


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Single mode users more inert than multi-modal users

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Very few transition between single-modal user patterns,the joint car+bicycle patterns acts as an intermediate step

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Strict car users have the same probability of moving towards the PT profileas joint car+bicycle users

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However, there is relatively much movement between the PT profile and the strict bicycle profile

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This is for the sample as a whole, but that are significant interactions!Solution: compute matrix for different subgroups

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Wave 2

Young (Mean - SD = 20.2)

Middle-aged (Mean=37.3)

Old (Mean + SD = 54.4)

SB SC LT JCB PT SB SC LT JCB PT SB SC LT JCB PT

Wave 1

DidNotMoveHouse

Male

SB0.79

0.04

0.05

0.05

0.07

0.62

0.10 0.10 0.13 0.05 0.750.01

0.18

0.02

0.04

SC0.03

0.79

0.04

0.09

0.05

0.01

0.84 0.06 0.08 0.02 0.000.83

0.08

0.07

0.01

LT0.28

0.24

0.34

0.04

0.10

0.08

0.27 0.46 0.15 0.04 0.050.20

0.66

0.07

0.02

JCB0.16

0.24

0.02

0.52

0.06

0.07

0.23 0.03 0.66 0.01 0.050.19

0.06

0.70

0.00

PT0.16

0.09

0.06

0.02

0.67

0.07

0.23 0.08 0.03 0.59 0.040.11

0.12

0.01

0.72

Female

SB0.78

0.01

0.07

0.05

0.09

0.62

0.02 0.14 0.14 0.07 0.690.00

0.23

0.02

0.05

SC0.06

0.67

0.09

0.14

0.04

0.02

0.71 0.14 0.12 0.01 0.010.68

0.19

0.11

0.01

LT0.37

0.09

0.40

0.03

0.12

0.12

0.11 0.61 0.10 0.05 0.070.08

0.79

0.04

0.03

JCB0.27

0.17

0.03

0.44

0.10

0.13

0.18 0.06 0.62 0.01 0.100.14

0.12

0.64

0.01

PT0.17

0.04

0.11

0.04

0.64

0.08

0.11 0.15 0.06 0.60 0.050.05

0.20

0.01

0.69

MovedHouse

Male

SB0.70

0.11

0.07

0.07

0.06

0.46

0.24 0.11 0.15 0.04 0.670.03

0.24

0.03

0.04

SC0.05

0.73

0.02

0.12

0.07

0.01

0.81 0.04 0.11 0.03 0.010.81

0.06

0.10

0.02

LT0.28

0.35

0.27

0.01

0.08

0.09

0.43 0.39 0.06 0.03 0.050.32

0.58

0.03

0.02

JCB0.15

0.24

0.02

0.45

0.14

0.07

0.25 0.03 0.63 0.01 0.060.20

0.06

0.67

0.01

PT0.33

0.03

0.07

0.04

0.54

0.16

0.10 0.10 0.07 0.57 0.110.05

0.14

0.01

0.69

Female

SB0.72

0.03

0.10

0.08

0.08

0.52

0.07 0.17 0.18 0.06 0.610.01

0.30

0.03

0.04

SC0.09

0.61

0.06

0.19

0.05

0.03

0.69 0.09 0.17 0.02 0.010.68

0.13

0.16

0.01

LT0.41

0.14

0.34

0.01

0.10

0.15

0.20 0.57 0.04 0.05 0.080.13

0.75

0.02

0.03

JCB0.23

0.16

0.03

0.36

0.22

0.13

0.19 0.06 0.59 0.02 0.100.15

0.12

0.62

0.02

PT0.33

0.01

0.11

0.06

0.49

0.17

0.04 0.16 0.11 0.51 0.110.02

0.23

0.02

0.62

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Old men who did not move house



Young women who did move house

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Conclusions• People’s travel behavior can be condensed into

five clusters.• The clusters point to different patterns of

complementarity and substitution between the modes.

• The research shows that multimodal users are more likely to switch than single-mode users.

• Younger people are generally less inert than older people

• People’s travel behavior becomes more in flux after a move

• For younger people it holds that the bicycle may aid in the transition from a car to PT profile.

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Reflection and future research

• LTA modeling requires (very) large sample sizes• Data in this study are old (25 years)• Explore influence of built environment / level of

service• Explore influence of other life events (children,

divorce, death of a family member, job change)• Explore influence of attitudes / lifestyle• Explore two-way interactions

1 modeling the behavioral determinants of travel behavior: an application of latent transition...

Documents

little use

pt demand bicycle demand

inert car demand

pt demand pt demand

multimodal travel demand

pt demand slide

car travel better pt

travel time