Atasoy, Bilge

Bierlaire, Michel

Glerum, Aurélie

Hurtubia, Ricardo

Nguyen, My Hang

Infoscience - École polytechnique fédérale de Lausanne

Aurélie Glerum Ricardo Hurtubia My Hang Nguyen Bilge Atasoy Michel Bierlaire DEMAND MODELS FOR TRANSPORTATION MODES    A FOCUS ON THE MEASUREMENT OF LATENT CONSTRUCTS AFFECTING DECISIONS   TLA/ToL  joint seminar KTH Royal Institute of Technology  Friday 12th October 2012 Introduction & motivation  Methodology  The data • Vehicle choice case study  • Mode choice case study   Incorporation of measurements into HCM • Vehicle choice case study (ICLV example) • Mode choice case study (ICLC example)  Conclusion OUTLINE 2 Recent developments in demand modeling for transportation  • Hybrid choice model (HCM) framework (Walker, 2001; Ben-Akiva et al., 2002)   Comprehensive framework that allows to incorporate unobservable factors      as explanatory variables of choice.     • Choice of transportion mode, car, etc. • Influenced by economic factors:   • Often also involve more subjective factors:    • HCM framework incorporates these subjective factors.      3 INTRODUCTION & MOTIVATION Discrete choice model (DCM) Latent variable model (LVM)  or  Latent class model (LCM) + • Price • Trip duration • Etc. • Attitudes • Perceptions • Lifestyles • Habits Figure extracted from Walker and Ben-Akiva, 2002. INTRODUCTION & MOTIVATION 4 Hybrid choice model (HCM): DCM with latent constructs.          Hybrid choice model (HCM): DCM with latent constructs. Latent construct can be… either a latent class model • Unobservable construct is discrete • Useful for segmentation according to lifestyle           Figure extracted from Walker and Ben-Akiva, 2002. INTRODUCTION & MOTIVATION 5 Hybrid choice model (HCM): DCM with latent constructs. Latent construct can be… or a latent variable model • Unobservable construct is continuous • Useful to analyze the impact of changes in prices across individuals  pricing          Figure extracted from Walker and Ben-Akiva, 2002. INTRODUCTION & MOTIVATION 6 INTRODUCTION & MOTIVATION 7 Important issues in the use of HCMs:  1. Measurement of latent variable / latent class     How to obtain the most realistic and accurate measure of an    attitude / perception / lifestyle?     Opinion statements: usual way in the literature  2. Integration of the measurement into the choice model     How to incorporate this information in the choice modeling    framework?        INTRODUCTION & MOTIVATION 8 Important issues in the use of HCMs:  1. Measurement of latent variable / latent class     How to obtain the most realistic and accurate measure of an    attitude / perception / lifestyle?     Opinion statements: usual way in the literature  2. Integration of the measurement into the choice model     How to incorporate this information in the choice modeling    framework?        Focus of this research: measurement model        METHODOLOGY 9 Integration of the measurement into the choice model          Latent construct Utilities Choice indicators Explanatory variables  Disturbances Disturbances Disturbances Measurement indicators METHODOLOGY 10 Integration of the measurement into the choice model:  • Structural equation model (SEM) framework used to characterize latent construct and relate it to its measurement indicators   (e.g. Bollen, 1989; Hancock and Mueller, 2006; Bartholomew et al., 2011).         Latent construct Utilities Choice indicators Explanatory variables  Disturbances Disturbances Disturbances Measurement indicators METHODOLOGY 11 Integration of the measurement into the choice model  • In transportation applications: • Heterogeneity of latent construct (e.g. attitude) captured among population • But: also need to capture heterogeneity in reporting indicators of latent construct         Latent construct Utilities Choice indicators Explanatory variables  Disturbances Disturbances Disturbances Measurement indicators METHODOLOGY 12 Integration of the measurement into the choice model  • In transportation applications: • Heterogeneity of latent construct (e.g. attitude) captured among population • But: also need to capture heterogeneity in reporting indicators of latent construct         Latent construct Utilities Choice indicators Explanatory variables  Disturbances Disturbances Disturbances Measurement indicators METHODOLOGY 13 Integration of the measurement into the choice model  • In transportation applications: • Heterogeneity of latent construct (e.g. attitude) captured among population • But: also need to capture heterogeneity in reporting indicators of latent construct         Latent construct Utilities Choice indicators Explanatory variables  Disturbances Disturbances Disturbances Measurement indicators Focus of this presentation        METHODOLOGY 14 Model specification  Likelihood function given by:                                                                  with  Integrated choice and latent variable model      Integrated choice and latent class model       ∏==Nninnin XIyfL1),,,;|,( ωσλβα∫ ⋅⋅= * **** ),;|();,|();,|(),,,;|,(ninX nnnninnyninininnin dXXXfXXIfXXyPXIyf ωω σλαβσλβα ==otherwise  0max  if  1 jnjininUUyinySsninninininnin XsPsXIPsXyPXIyP⋅⋅= ∑∈),;|();,|();,|(),,,;|,( ωω σλαβσλβαMETHODOLOGY 15 Model specification  Likelihood function given by:                                                                  with  Integrated choice and latent variable model      Integrated choice and latent class model       ∏==Nninnin XIyfL1),,,;|,( ωσλβα∫ ⋅⋅= * **** ),;|();,|();,|(),,,;|,(ninX nnnninnyninininnin dXXXfXXIfXXyPXIyf ωω σλαβσλβα ==otherwise  0max  if  1 jnjininUUyinySsninninininnin XsPsXIPsXyPXIyP⋅⋅= ∑∈),;|();,|();,|(),,,;|,( ωω σλαβσλβαFew examples that incorporate socio-economic information into the measurement model  THE DATA 16 Two case studies:   1. Integrated choice and latent variable model (ICLV): analysis of the impact of pro-convenience attitude on choice of car.      2. Integrated choice and latent class model (ICLC): analysis of the transportation mode choices for individuals segmented according to dependent / independent classes.        Car purchase choice case study     Mode choice case study     THE DATA 17 Choice Gasoline / diesel  New alternative: Electric  Competitors  Renault  Renault   VEHICLE CHOICE CASE STUDY  Stated preferences (SP) survey:  • Car purchase choice study  • Conducted in Switzerland in 2011 among individuals who bought a new car recently or intend to buy one soon.  • Conducted with Renault Suisse SA.  • Customized choice situations  • 693 questionnaires obtained           THE DATA 18  VEHICLE CHOICE CASE STUDY   • Environmental concern  • Attitude towards new technologies  • Perception of the reliability of an electric vehicle  • Perception of leasing  • Attitude towards design  Ratings • Total disagreement (1) • Disagreement (2) • Neutral opinion (3) • Agreement (4) • Total agreement (5) • I don’t know (6)        An electric car is a 100% ecological solution.  A control screen is essential in my use of a car.  Electric cars are not as secure as gasoline cars.  Leasing is an optimal contract which allows me to change car frequently.  Design is a secondary element when purchasing a car, which is above all a practical transport mode. Opinion statements related to five themes THE DATA 19  Revealed preferences (RP) survey  • Mode choice study  • Conducted between 2009-2010 in low-density areas of Switzerland  • Conducted with PostBus (major bus company in Switzerland, operates in low-density areas)  • Info on all trips performed by inhabitants in one day: • Transport mode • Trip duration • Cost of trip • Activity at destination • Etc.  • 1763 valid questionnaires collected           Choice MODE CHOICE CASE STUDY THE DATA 20 Opinion statements related to four themes  • Environment  • Mobility  • Residential choice  • Lifestyle   Ratings • Total disagreement (1) • Disagreement (2) • Neutral opinion (3) • Agreement (4) • Total agreement (5) • I don’t know (6)       MODE CHOICE CASE STUDY  The price of gasoline should be increased in order to reduce traffic congestion and air pollution.  Taking the bus helps making a town more comfortable and welcoming.  Accessibility and mobility conditions are important in the choice of an accommodation.  I always plan my activities a long time in advance. Role of indicators of latent construct: • Measure a latent variable • Enhance a latent class model  Issue: biases in the measurement of indicators due to heterogeneity of response behavior  By introducing socio-economic information into the measurement component of the HCM, the bias is reduced.  Two examples: • Car choice case study (ICLM):  capture exaggeration effects in responses to indicators. • Transportation mode choice case study (ICLC): capture bias in responses to indicators due to various socio-economic characteristics.  21 INCORPORATION OF MEASUREMENTS INTO HCM INCORPORATION OF MEASUREMENTS INTO HCM 22  Motivation for integration of explanatory factors of measurement indicators:  • Dispersion effects: • Exaggeration effects in experiments on survey design in social science literature (Schuman and Presser, 1996) • Some individuals tend to report responses at extremities of scale of agreement though their commitment to the opinion statement is not strong.  • Socio-economic characteristics might explain different response behaviors                        Need to account for heterogeneity of response behavior      INCORPORATION OF MEASUREMENTS INTO HCM 23    1. Integrated choice and latent variable model (ICLV): analysis of the impact of pro-convenience attitude on choice of car.          Vehicle choice case study     VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) Choice Gasoline / diesel  New alternative: Electric  Competitors  Renault  Renault  INCORPORATION OF MEASUREMENTS INTO HCM 24 Battery lease Incentive Gasoline / electricity costs Purchase price Utility Explanatory variables Competitors gasoline, Renault gasoline, Renault electric Choice Socio-economic characteristics Pro-convenience attitude Indicators Design is secondary, a car is a practical transport mode. Spaciousness / capacity more important than look. New propulsion technology more important than look. Explanatory variables VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) Gender Number of people in household Age Retired Owner Electric car model Explanatory variables Exaggeration effects Latent variable model Discrete choice model 25 Definition of index:   • Definition of degree of extremity        with    • En : number of occurrences of ‘total disagreement’ and ‘total agreement’ for individual n over all R opinion questions of the survey   INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) ∑==Rrrnn JE1  ===otherwise   05or  1 if    1 rnrnrnIIJ26 Definition of scale parameter:   • Measurement model:      • Scale that captures heterogeneity in response behavior:   INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) )()1(1 nEE EII Extnnn νθθν σσ ⋅−+⋅= <<γθθ ⋅⋅−+⋅= << nEE EII nn )1(1nnn XmI υα += );(**),0(~nLogisticn υσυ27 Definition of scale parameter:   • Measurement model:      • Scale that captures heterogeneity in response behavior:   INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) )()1(1 nEE EII Extnnn νθθν σσ ⋅−+⋅= <<γθθ ⋅⋅−+⋅= << nEE EII nn )1(1nnn XmI υα += );(**),0(~nLogisticn υσυDefine threshold θ above which individuals show extreme behavior Statistical analyses show that highest fit for θ = 7. 28 Definition of scale parameter:   • Measurement model:      • Scale that captures heterogeneity in response behavior:   INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) )()1(1 nEE EII Extnnn νθθν σσ ⋅−+⋅= <<γθθ ⋅⋅−+⋅= << nEE EII nn )1(1nnn XmI υα += );(**),0(~nLogisticn υσυGroup-specific scale 29 Definition of scale parameter:   • Measurement model:      • Scale that captures heterogeneity in response behavior:   INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) )()1(1 nEE EII Extnnn νθθν σσ ⋅−+⋅= <<γθθ ⋅⋅−+⋅= << nEE EII nn )1(1nnn XmI υα += );(**),0(~nLogisticn υσυProgressive scale:  • The higher the degree of extremity, the higher the scale. • γ parameter to estimate Results for the latent variable model              Simultaneous estimation of the HCM using the extended version of Biogeme (Bierlaire and Fetiarison, 2009) 30 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) Results from the latent variable model 31 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) γσ θθυ ⋅⋅−+⋅= << nEE EII nnn )1(1Results from the latent variable model               We observe dispersion effects, since for the ‘extreme’ group we have: 32 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) γσ θθυ ⋅⋅−+⋅= << nEE EII nnn )1(142.17 =⋅= γσυnResults from the choice model 33 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) Pro-convenience attitude significantly affects car choice.    Improvement of fit over model without dispersion effects 34 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE) INCORPORATION OF MEASUREMENTS INTO HCM 35   2. Integrated choice and latent class model (ICLC): analysis of the transportation mode choices for individuals segmented according to dependent / independent classes.        Mode choice case study     Choice MODE CHOICE CASE STUDY (ICLC EXAMPLE) 36 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) Latent classes High income Utility Family Single Independent Hard to take PT when I travel with my children. Dependent Latent class model Class-specific choice model Indicators Number of children Number of cars Number of bikes Student Travel time Travel cost Trip purpose Distance French part vs German part Urban vs rural Explanatory variables Explanatory variables With my car, I can go where I want when I want.  I would like to spend more time with my family and friends. Children Number of cars Full time job Couples + children Couples - children Single parents Explanatory variables Private motorized modes (PMM),  Public transportation (PT), Soft modes (SM) Choice Class-specific measurement equations:           37 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) Class-specific measurement equations:  Class-specific parameters           38 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) Class-specific measurement equations:  Class-specific parameters            Socio-economic information as explanatory variables of response to indicators           39 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) Estimation results for ICLC          • Increase of the significance of the parameters of the latent class model.  40 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) Estimation results for LCCM          • Increase of the significance of the parameters of the latent class model.  • Income parameter has become more important. 41 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) Model application: computation of VOT            • VOTs comparable with literature on transport economics (Jara-Diaz, 2007), where VOT can be compared to wage rate. • Individuals in the independent class have higher incomes (> 8000 CHF), hence a higher value of time.  42 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE) CONCLUSION Main findings:   • Heterogeneity of response behavior exists and can be captured by individual-specific information in measurement model • Evidence for the importance of accounting for it:  • ICLV model of car choice: • Significant scale parameter • Increases as degree of extremity increases  • ICLC model of mode choice: • Socio-economic characteristics affect response to opinion questions significantly • Parameters of the class membership utility increase in significance • VOT are comparable with existing studies   43       Thanks! 44 

Demand models for transportation modes: a focus on the measurement of latent constructs affecting decisions

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Demand models for transportation modes: a focus on the measurement of latent constructs affecting decisions

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