1 wp 2 “automobile” the relationship between the specific (dis)utility and the frequency of...
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WP 2 “Automobile”
The relationship between the specific
(dis)utility and the frequency of driving a car
Marco DianaMarco Diana
Politecnico di Torino, ITALYPolitecnico di Torino, ITALY
FUNDP, Namur, 2FUNDP, Namur, 2ndnd December 2004 December 2004
COST action 355 WATCHCOST action 355 WATCH
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Transport planning modelsTransport planning models
1. Trip-based models: trips and traffic flows are at the core of the modelling effort (4 steps…)
2. Activity-based models: transport demand is derived from underlying activity patterns
however…
3. A too rigid interpretation of the “activity-based paradigm” seems not appropriate:
a) (Individual) travel time budgets
b) “Irrational” travel behaviours and choices
c) Telework-commute trips complementarity
d) Research work by Mokhtarian et al. (2001)
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Some results from previous researchesSome results from previous researches
0%
5%
10%
15%
20%
25%
30%
35%
0-4
5-9
10
-14
15
-19
20
-24
25
-29
30
-34
35
-39
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-44
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-54
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-59
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-69
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-74
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-79
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-84
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-89
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-94
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-99
>9
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Actual commute time
Ideal commute time
Mohktarian (2001a)
Yes
No
Gaia (2003): Teleportation test
0% 10% 20% 30% 40% 50% 60% 70%
Travel time isgenerally wasted
time
I use my commutetime productively
The only good thingis arriving atdestination
Agree
Neutral
Disagree
Mohktarian (2001b)
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Research objectiveResearch objective
Final goal: the assessment of the influence of the primary utility of travel (if any) on travel behaviour, against the influence of the derived utility
Milestones of our research:– Endogenous variable: car driving frequency– Data source: NTAUS (2002)– Considering mode-related primary utility
Primary utility = portion of the total utilitynot dependent on the fact of leaving
a place to reach another place
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Methodological challengesMethodological challenges
We face a serious measurement problem: primary and derived utility are often confounded by respondents
Idea: to focus on the presence of reported difficulties and limiting behaviours while driving
Assumption: the above affects the primary more than the derived utility of travel
Limitation: the method works only in presence of difficulties and limiting behaviours
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Case study: the NTAUS datasetCase study: the NTAUS dataset
In 2002, the National Transportation Availability and Use Survey (NTAUS) has been carried out in the U.S.
5019 completed surveys (about half with persons with disabilities)
Not a classical mobility survey. Covered topics: Trip frequencies per mode
Household vehicles ownership and use
Experiences, opinions and difficulties related to the use of different transport modes
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Methodological stepsMethodological steps
1. Define a suitable measurement model for the “primary utility” construct
2. Define causal interrelationships between:
Socioeconomic variables
Primary utility
Driving frequency
Simultaneous estimation of the measurement and of the structural models through a
structural equation modelling technique
Exploratory factorial analyses
Literature search
Statistical confir-mation
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EFA for the measurement model (1/2)EFA for the measurement model (1/2)
Driving-related fitness:
“The following is worse/same/better than 5 years ago:”
1. Eyesight or night vision
2. Attention span
3. Hearing
4. Coordination
5. Reaction time to brake or swerve
6. Depth perception
Driving-related fitness:
“The following is worse/same/better than 5 years ago:”
1. ……………………….…….. 0.504
2. ……………………………………….. 0.655
3. ……………………………………………….. 0.573
4. …………………………………………. 0.755
5. ………………… 0.750
6. ……………………………………. 0.685
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EFA for the measurement model (2/2)EFA for the measurement model (2/2)
Driving self-limitations: “Do you usually…”1. Drive less than you used to
2. Avoid driving at night
3. Drive less in bad weather
4. Avoid high-speed roads and highways
5. Avoid busy roads and intersections
6. Drive slower than the posted speed limits
7. Avoid left-hand turns
8. Avoid driving during rush hour
9. Avoid driving on unfamiliar roads or to unfamiliar places
10. Avoid driving distances of over 100 miles
Driving self-limitations: “Do you usually…”1. ……………………………….……. 0.533
2. ……………………………………………. 0.673
3. ………………………………..….….. 0.616
4. ……………………….. 0.696
5. …………………………… 0.650
6. ……………………. 0.371
7. ……………………………………………. 0.371
8. ………………………………… 0.615
9. …… 0.690
10. ……………………. 0.679
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Rationale of the structural modelRationale of the structural model
General model structure: research on the relationships between socioeconomic characteristics, attitudes, perceptions and choice
Socioeconomic variables influence primary utility and driving frequency; primary utility also influences driving frequency
No feedback loops are modeled at this stage (hierarchical and recursive model)
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Structural model path diagramStructural model path diagram
Driving frequency
Driving-related fitness
Driving self-limitations
Number of household vehicles
Presence of modified vehicles
Gender
Age
Physical impairments:-None-Mild-Moderate-Severe
Household kind:-Lives alone-Lives with spouse-Lives with kids-Lives with parents-Lives with others
Income:-Less than $15,000-$15,000 – $50,000-Over $50,000
Needs help to travel
Unavailable transport
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Number of
vehicles
Modified vehicles
Driving-related fitness
Driving self-
limitations
Driving frequency
Male -0.0796 0.0648 Age * 0.0579 -0.1598 -0.0925 -0.0966 Income: $15,000-$50,000 0.0577 0.1424 * Income: over $50,000 0.2154 0.1467 * Lives alone -0.1089 * Lives with spouse 0.1879 -0.1004 Lives with kids 0.0726 0.0497 Lives with parents 0.2030 -0.0951 Lives with others 0.1368 * Mild impairments -0.0430 -0.0366 0.6403 * Moderate impairments * -0.0655 0.5439 -0.0940 Severe impairments 0.0624 -0.0741 1.1285 -0.0971 Needs help to travel 0.2203 * -0.2997 -0.0898 Needed transport not available 0.0444 * -0.1643 -0.0464 Number of vehicles 0.0448 Modified vehicles * * Driving-related fitness -0.1432 * Driving self-limitations -0.6126
Estimation results: direct effectsEstimation results: direct effects
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Total effects on Total effects on driving frequencydriving frequency
Male 0.1136 Mild impairments -0.4164
Age -0.0657 Moderate impairments -0.4375
Income: $15,000-$50,000 0.0000 Severe impairments -0.7998
Income: over $50,000 0.0758 Needs help to travel 0.0955
Lives alone 0.0345 Needed transport not avail. 0.0523
Lives with spouse -0.0920 Number of household veh. 0.0448
Lives with kids 0.0530 Presence of modified veh. 0.0049
Lives with parents -0.0860 Driving-related fitness 0.1575
Lives with others -0.0340 Driving self-limitations -0.6126
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Not considering the primary utilityNot considering the primary utility
Driving frequency
Number of household vehicles
Presence of modified vehicles
Gender
Age
Physical impairments:-None-Mild-Moderate-Severe
Household kind:-Lives alone-Lives with spouse-Lives with kids-Lives with parents-Lives with others
Income:-Less than $15,000-$15,000 – $50,000-Over $50,000
Needs help to travel
Unavailable transport
Overfitting model
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Conclusions and future workConclusions and future work
The importance of the primary utility of travel seems confirmed within the selected framework
Case study: to combine specific information on primary utility with a classical mobility survey dataset
Measurement model: to define constructs for people that do not report difficulties or limiting behaviors
Structural model: to have a better representation of cognitive processes (feedback from driving frequency)
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Thank youThank you
The relationship between the specific (dis)utility and the frequency of driving a car
Research carried out in collaboration with INRETS – DEST
To be presented at:84th TRB Annual Meeting
Washington, D.C., 9-13 January 2005
Marco Diana