demand models for transportation modes · incorporation of measurements into hcm • vehicle choice...
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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
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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
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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
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Figure extracted from Walker and Ben-Akiva, 2002.
INTRODUCTION & MOTIVATION 4
Hybrid choice model (HCM): DCM with latent constructs.
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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
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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
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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?
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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
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METHODOLOGY 9
Integration of the measurement into the choice model
Latent construct Utilities
Choice indicators
Explanatory variables
Disturbances
Disturbances
Disturbances
Measurement indicators
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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
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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
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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
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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
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METHODOLOGY 14
Model specification Likelihood function given by: with Integrated choice and latent variable model Integrated choice and latent class model
∏=
=N
ninnin XIyfL
1
),,,;|,( ωσλβα
∫ ⋅⋅=*
**** ),;|();,|();,|(),,,;|,(n
in
X nnnninny
ninininnin dXXXfXXIfXXyPXIyf ωω σλαβσλβα
=
=otherwise 0
max if 1 jnjinin
UUy
iny
Ssninninininnin XsPsXIPsXyPXIyP
⋅⋅= ∑∈
),;|();,|();,|(),,,;|,( ωω σλαβσλβα
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METHODOLOGY 15
Model specification Likelihood function given by: with Integrated choice and latent variable model Integrated choice and latent class model
∏=
=N
ninnin XIyfL
1
),,,;|,( ωσλβα
∫ ⋅⋅=*
**** ),;|();,|();,|(),,,;|,(n
in
X nnnninny
ninininnin dXXXfXXIfXXyPXIyf ωω σλαβσλβα
=
=otherwise 0
max if 1 jnjinin
UUy
iny
Ssninninininnin XsPsXIPsXyPXIyP
⋅⋅= ∑∈
),;|();,|();,|(),,,;|,( ωω σλαβσλβα
Few examples that incorporate socio-economic information into the measurement model
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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)
∑=
=R
rrnn JE
1 ==
=otherwise 0
5or 1 if 1 rnrnrn
IIJ
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26
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 EIIExtnnn νθθν σσ ⋅−+⋅= <<
γθθ ⋅⋅−+⋅= << nEE EIInn
)1(1
nnn XmI υα += );( **
),0(~n
Logisticn υσυ
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27
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 EIIExtnnn νθθν σσ ⋅−+⋅= <<
γθθ ⋅⋅−+⋅= << nEE EIInn
)1(1
nnn XmI υα += );( **
),0(~n
Logisticn υσυ
Define threshold θ above which individuals show extreme behavior Statistical analyses show that highest fit for θ = 7.
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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 EIIExtnnn νθθν σσ ⋅−+⋅= <<
γθθ ⋅⋅−+⋅= << nEE EIInn
)1(1
nnn XmI υα += );( **
),0(~n
Logisticn υσυ
Group-specific scale
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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 EIIExtnnn νθθν σσ ⋅−+⋅= <<
γθθ ⋅⋅−+⋅= << nEE EIInn
)1(1
nnn XmI υα += );( **
),0(~n
Logisticn υσυ
Progressive scale: • The higher the degree of extremity, the higher the scale. • γ parameter to estimate
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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)
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Results from the latent variable model
31 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE)
γσ θθυ ⋅⋅−+⋅= << nEE EIInnn
)1(1
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Results 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 EIInnn
)1(1
42.17 =⋅= γσυn
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Results from the choice model
33 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE)
Pro-convenience attitude significantly affects car choice.
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Improvement of fit over model without dispersion effects
34 INCORPORATION OF MEASUREMENTS INTO HCM VEHICLE CHOICE CASE STUDY (ICLV EXAMPLE)
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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)
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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
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Class-specific measurement equations:
37 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE)
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Class-specific measurement equations: Class-specific parameters
38 INCORPORATION OF MEASUREMENTS INTO HCM MODE CHOICE CASE STUDY (ICLC EXAMPLE)
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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)
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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)
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Estimation results for LCCM • Increase of the significance of the parameters of the latent class
model.
• Income parameter has become more important.
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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.
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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
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Thanks!
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