Attitudes in SP Surveys

Transcription

1 Attitudes in SP Surveys Basil Schmid IVT ETH Zurich Measurement and Modeling FS2016

2 Outline 1. Introduction 2. Attitudes in choice models: A case study Attitudes in SP Surveys 2

3 Introduction Apart from the attributes that are created by design, researchers usually include socio-demographic characteristics (age, sex, area of living, education, income, etc.) in their choice models Growing interest in personal attitudes and perceptions that come on top of fundamental characteristics, and are hypothesized to affect choices Measurement of attitudes via self-reported psychometric Likert-scale questionnaires: Often by presenting some statements, followed by check-boxes within a range from completely disagree to completely agree One might add some of these questions/statements after the actual choice experiment Attitudes in SP Surveys 3

4 Questionnaire example: Post-Car World Attitudes in SP Surveys 4

5 Questionnaire example: Post-Car World Attitudes in SP Surveys 5

6 Attitudes in choice models: A case study Measures of different statements regarding car ownership public infrastructure and regulations environmental concerns public transport affinity walk and bike opportunities hypothetical transport modes Exploratory factor analysis to... reduce the dimensionality of data to the most essential elements remove sources of covariance and measurement noise estimate uncorrelated factor scores with µ 0 and σ 1 Attitudes in SP Surveys 6

7 Outliers detection Euclidean Distance (L2) ,000 1,200 1,400 Attitudes Outlier Detection 162 subjects, 24 questionnaire items KA531 Attitudes in SP Surveys 7

8 Factor analysis Attitudes in SP Surveys 8

9 Factor analysis Questionnaire item Factor 1 Factor 2 Factor 3 1. Social disadvantage without car Car as a status symbol Decrease speed for lower pollution Higher fuel prices should subsidize PT My car should be extraordinary Daily life without car is impossible Car driving is bad for the environment PT should get priority in the traffic Meeting unpleasant people in PT PT is not flexible enough PT timetables are too complicated Too much noise and pollution for walking Driving a bike is a feeling of freedom Driving a bike is the best travel mode Noise of car engines provides good feelings Prices should increase to reduce car use Carsharing services should be improved More investments into autonomous cars Autonomous cars would be a good alternative Autonomous cars are scary Moving pathways as a main mode in cities Reduction in traffic by lowering immigration Zurich without cars is inconceivable Everything should remain as it is 0.31 Attitudes in SP Surveys 9

10 Factor analysis 3 factors retained: ENVISENSI: Desired reduction of car use mainly through prices and regulations, favoring better pedestrian and public infrastructure CONSCAR: Endorsement of car use and pleasure of driving, showing general reluctance towards PT and any kind of major structural changes TECHLOVE: Openness towards new transportation technologies (moving pathways, autonomous cars) Explained variance = 60 % Scale reliability: Cronbach s α = 0.8 Sampling adequacy: KMO = 0.75 Attitudes in SP Surveys 10

11 Correlation Matrix ENVI CONS TECH Sex CS Seas. GA Age Inc. Car av. ENVISENSI 1 CONSCAR 1 TECHLOVE 1 Male 1 CS member Season tick GA Age Income Car avail Understanding the correlation structure in the data Pearson correlation coefficients indicate strong interdependencies (N = 162; bold: p < 0.01) Income and attitudes are uncorrelated Starting point for Hybrid Choice approach (more on that in the lecture on the 12th of May) Attitudes in SP Surveys 11

12 Modeling Framework RUM: Choice of mode i by maximizing utility U: U V = β tt,v X tt,v + ɛ V (1) where: ( ) Xinc λinc U CP = β tt,cp X tt,cp + β tc X tc,cp + βacc X acc,cp x inc (2) + β risk X risk,cp + ɛ CP ( ) λinc Xinc U CS = β tt,cs X tt,cs + β tc X tc,cs + βacc X acc,cs + ɛ CS (3) x inc ) ( ) Xinc λinc U PT = β tt,pt X tt,pt + β tc X tc,pt + βacc X acc,pt x inc (4) + β tr X tr,pt + β head X head,pt + ɛ PT β j and λ inc are the parameters to be estimated X j is the value vector of attribute j Attitudes in SP Surveys 12

13 Modeling Framework V G = β age X age + δ caralways X caralways + δ PT X PT + φ 1 ENVISENSI + φ 2 CONSCAR + φ 3 TECHLOVE + υ tc,envisensi (X tc ENVISENSI) + υ tc,conscar (X tc CONSCAR) + υ tc,techlove (X tc TECHLOVE) (5) where: β s, δ s, φ s and υ s are the parameters to be estimated X s is the value vector of characteristic s ENVISENSI, CONSCAR and TECHLOVE are the factor scores of the attitudes Attitudes in SP Surveys 13

14 Modeling Framework VTTS of mode i as a function of income and factor scores: β tt,i 60 VTTS i (inc, att) = ( ) CHF/h Xinc λinc β tc + υtc,att att x inc (6) For the average respondent, the equation collapses to the standard VTTS notation Attitudes in SP Surveys 14

15 Mode Choice Results Base category: PT Model 1 Model 2 Model 3 Model 4 Travel time carpooling (CP) Travel time carsharing (CS) Travel time pub. transport (PT) Travel time Bike (Bike) Travel cost Income elasticity of travel cost Travel cost x ENVISENSI Travel cost x CONSCAR Travel cost x TECHLOVE Access and egress time Risk to miss driver (CP) Transfers (PT) Service interval (PT) Parameters have the expected signs and are significant Mode-specific valuation of travel time Negative income elasticity of travel cost Car lovers exhibit lower travel cost sensitivity Attitudes in SP Surveys 15

16 Mode Choice Results Base category: PT Model 1 Model 2 Model 3 Model 4 ASC CP Age (CP) Car always available (CP) Season ticket (CP) ENVISENSI (CP) CONSCAR (CP) TECHLOVE (CP) ASC CS Age (CS) Car always available (CS) Season ticket (CS) ENVISENSI (CS) CONSCAR (CS) TECHLOVE (CS) ASC Bike Age (Bike) Car always available (Bike) Season ticket (Bike) ENVISENSI (Bike) CONSCAR (Bike) TECHLOVE (Bike) Attitudes in SP Surveys 16

17 Mode Choice Results Base category: PT Model 1 Model 2 Model 3 Model 4 AICc Number of estimated parameters Number of observations Number of subjects ρ VTTS carpooling [CHF/h] VTTS carsharing [CHF/h] VTTS PT [CHF/h] VTTS bike [CHF/h] PT travel time per transfer [min] WTP for avoiding transfer [CHF] PT travel time per access time [-] CP travel time per risk of missing [min/%] WTP for CP risk reduction [CHF/%] Model 3 is the winner in terms of AICc Substantial part of unobserved heterogeneity is captured by the latent variables VTTS lie in the range between 12 (CS) and 49 (Bike) CHF/h Attitudes in SP Surveys 17

18 Extensions, Conclusions and Outlook Mixed Logit approach: AICc would significantly increase from model 1 to 3 = random, unobserved components are captured by the latent variables Car lovers: Lower cost sensitivity not resulting from - non-trading behavior - more frequent choice of expensive CS - higher income possible explanation: Extreme dislike of slow modes Mode-specific travel time x attitudes interactions Positive quadratic time and cost effects: Marginal dis-utilities of travel time and costs are decreasing Combining with RP data Attitudes in SP Surveys 18