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1 Modeling the behavioral determinants of travel behavior: an application of latent transition analysis Maarten Kroesen Section Transport and Logistics.

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Presentation on theme: "1 Modeling the behavioral determinants of travel behavior: an application of latent transition analysis Maarten Kroesen Section Transport and Logistics."— Presentation transcript:

1 1 Modeling the behavioral determinants of travel behavior: an application of latent transition analysis Maarten Kroesen Section Transport and Logistics (TLO)

2 Transportation: a mixed blessing 2

3 Sustainable mobility 3

4 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… 4 Two types of questions

5 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, Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e

6 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, Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e Insights: Travel behavior is generally inert ++

7 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, Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e Insights: Travel behavior is generally inert Car demand is affected by bicycle demand, but not by PT demand ++ -

8 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, Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e Insights: Travel behavior is generally inert Car demand is affected by bicycle demand, but not by PT demand Bicycle demand is affected by car and PT demand

9 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, Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e Insights: Travel behavior is generally inert Car demand is affected by bicycle 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

10 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, Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 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)?

11 An alternative conceptualization 11 Travel behavior patterns Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e e e e Travel behavior patterns Sex Age Moved house Year 2 Year 1ABC Travel pattern APa  aPa  bPa  c Travel pattern BPb  aPb  bPb  c Travel pattern CPc  aPc  bPc  c Matrix of transition probabilities LCM Latent transition model

12 Data The Dutch mobility panel 10 bi-annual waves (March and September) from 1984 to individuals per wave Analysis was based on 6 March waves Data were pooled into 2 waves N=5, Year 1Year 2Year 3Year 4Year 5Year 6 x1x2x3x4 y1y2y3 z1z2z3z4 Wave 1Wave 2 x1x2 y1y2 z2z3

13 Descriptive statistics 13 Variable Wave 1Wave 2 Weekly trips by carMean (SD)7.2 (9.5)7.1 (9.3) Weekly trips by bicycleMean (SD)7.5 (8.6)7.0 (8.4) Weekly trips by public transportMean (SD)1.4 (3.4)1.3 (3.2) Sex (%) Male50 Female50 AgeMean (SD)37.3 (17.1)38.3 (17.1) Education level (%) High school / vocational education 7978 Higher education2021 Income (%) ,000 guilders , ,000 guilders37 >34,000 guilders57 Missing67 Occupational status (%) Works in government1213 Works in company or self- employed 2930 Student2221 Works in household2221 Retiree78 Other88 Missing00 City type (%) Small city7677 Big city (Amsterdam or Rotterdam) 2423 Car license holder (%) No4038 Yes6062 Number of cars in household (%) or more1416 Train season-ticket holder (%) No9697 Yes43 Moved house (%) No87 Yes13

14 Distributions (N=5,314) 14 Trips by car Trips by bicycleTrips by PT Count variables  integer and positive

15 Latent class model Travel behavior patterns 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

16 Finding the optimal number of classes 16 Bivariate residuals N=5,314 Number of classes LLL²dfp-value % Reduction in L 2 (H 0 ) car- bicycle car-public bicycle- public Wave Wave <3.84  n.s.

17 17 N=5,314Class12345 Class size (%) Indicators Car trip ratePoisson mean Bicycle trip ratePoisson mean Public transport trip ratePoisson mean class solution: indicator profiles

18 Latent class model with covariates 18 Travel behavior patterns Car use Bicycle use Public transport use Year1 e e e Sex Age Moved house LCM Multinomial logit (MNL) model

19 19 N=5,314 (wave 1)Class12345 Class size (%) Indicators Car trip rateMean Bicycle trip rateMean Public transport trip rateMean Active covariates Sex (%) Male Female AgeMean Moved house (%) No Yes Inactive covariates Education level (%) High school / vocational education Higher education Income (%) ,000 guilders , ,000 guilders >34,000 guilders Missing85736 Occupational status (%) Works in government Works in company or self- employed Student Works in household Retiree Other Community type (%) Small city Big city (Amsterdam or Rotterdam) Car license (%) No Yes Number of cars in household (%) or more Train season ticket (%) No Yes Latent class profiles

20 20

21 Latent transition model with covariates 21 Travel behavior patterns Car use Bicycle use Public transport use Car use Bicycle use Public transport use Year1Year2 e e e e e e Travel behavior patterns Sex Age Moved house MNL model

22 22 Wave 2 Wave 1ParameterSBSCLTJCBPT (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 Age (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 Slope1.59 (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 Age (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 Slope2.58 (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 Age (standardized)-0.15 (-0.70)0.64 (3.04)1.07 (5.32)0.97 (3.17)0 Age (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 Slope3.74 (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 Age (-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.)00000 Sex (female)0.10 (0.35)-0.76 (-1.99)0.60 (1.79)0.64 (1.03)0 Age (standardized)-0.68 (-3.50)0.08 (0.28)0.26 (1.68)-0.67 (-1.09)0 Age (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

23 23 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Matrix of transition probabilities

24 24 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Matrix of transition probabilities Single mode users more inert than multi-modal users

25 25 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Matrix of transition probabilities Very few transition between single-modal user patterns, the joint car+bicycle patterns acts as an intermediate step

26 26 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Matrix of transition probabilities Strict car users have the same probability of moving towards the PT profile as joint car+bicycle users

27 27 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Matrix of transition probabilities However, there is relatively much movement between the PT profile and the strict bicycle profile

28 28 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Matrix of transition probabilities This is for the sample as a whole, but that are significant interactions! Solution: compute matrix for different subgroups

29 29 Wave 2 Young (Mean - SD = 20.2) Middle-aged (Mean=37.3) Old (Mean + SD = 54.4) SBSCLTJCBPTSBSCLTJCBPTSBSCLTJCBPT Wave 1 Did Not Move House Male SB SC LT JCB PT Female SB SC LT JCB PT Moved House Male SB SC LT JCB PT Female SB SC LT JCB PT

30 30 (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Old men who did not move house (N=5,314)Wave 2 Wave 1SBSCLTJCBPT Strict bicycle user Strict car user Light traveler Joint car and bicycle user Public transport user Young women who did move house

31 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. 31

32 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 32


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