Every five years, the Swiss Federal Offices for Spatial Development (ARE) and Statistics (BFS) carry out the Mobility and Transport Microcensus (MTMC), a one-day CATI diary survey representative of the Swiss population in terms of socio-economics and trip characteristics. In the year 2015 (for the second time after 2010), a Stated Preference (SP) survey linked to the MTMC was carried out. Respondents selected and recruited during the MCMT interviews were asked to answer a follow-up paper and pencil questionnaire. This later survey instrument included items from a combination of mode and route choice experiments based on one of the trips the respondents had reported during the MTMC CATI interview. The data, in combination with the Revealed Preference (RP) source of the MTMC, are primarily used in transport policy projects and for estimating mode and route choice models. Thus, they allow updates of regional and national transport models to current behavioral tendencies, and serve to obtain valuations of supply variables (travel times, etc.) to be used in cost-benefit analyses. The SP methodology allows for an assessment of respondents’ behavioral changes relative to changes in several different areas of the transportation systems. In addition, surveying information with similar SP-instruments in the years 2010 und 2015 allows an analysis of changes in peoples’ preferences and willingness to pay between the two years. The goals were: 1) design and carry out an SP survey that would closely resemble the MTMC in regards to the trip characteristics (mode, purpose, length) as well as the spatial and socio-economic properties of the respondents. This is of importance as the MCMT-survey population itself is with around 60´000 respondents representative for the Swiss residential population. 2) offer at the same time enough variation in all attributes in order to estimate significant parameters in the ensuing choice models and thus synchronize two important data sources commonly used for constructing and calibrating transport models. For the latter reason, priority was given to longer trips and trip purposes that have lower shares in the RP data. 3) design an SP experiment, which includes familiar and realistic situations for the respondents to collect reliable information. Filling out a survey based on their own RP-trips was expected to increase respondents´ interest, encourage them to imagine the presented alternatives, and reduce fatigue effects. As SP surveys induce substantial amounts of response burden by asking participants to imagine fictive situations and report their decisions, these issues are of high importance. 4) allow an analysis of the evolution of behavioral preferences as the SP experiments from the years 2010 and 2015 are comparable.

1. The Swiss National Stated Preference Study on
Transport Behavior 2015
Claude Weis (TransOptima GmbH)
Matthias Kowald (RheinMain University of Applied Sciences)
Antonin Danalet (ARE)
Basil Schmid (ETH Zurich)
Milos Balac (ETH Zurich)
Milenko Vrtic (TransOptima GmbH)
Nicole Mathys (ARE)
Kay Axhausen (ETH Zurich)
July 2018

2. The Swiss SP-study 2015
• Mode choices (HPM, PMT, PT) and route choices (PMT, PT)
• On a national level;
• Based on respondents´ RP trips;
• Allows to identify socio-economic and geographic effects;
• Allows to identify behavioral trends (with SP 2010).
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3. The Swiss Mobility and Transport Microcensus (MTMC)
• Conducted every five years (~60´000 participants);
• Sampling frame: Swiss population (aged 6 and above);
• Daily mobility (one day CATI diary survey);
• Stage based with route-recording during the interview (PMT &
PT);
• Since 2010: follow-up stated preference (SP) paper and pencil
questionnaire on mode and route choice.
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4. Sampling frame for SP survey
• Recruitment during MTMC interviews (~4´000 adults);
• Considered trips: work, education, shopping, business, leisure
• Sample one RP-trip/respondent:
• employing familiar and realistic situations for respondents;
• matches MTMC trip characteristics (mode, purpose and
length);
• matches MTMC respondents´ in spatial and socio-demographic
properties.
• SP provide a reliable and valid basis for transport policy and
models.
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5. Experimental design: Mode Choice
Alternative Attribute Variation of empirical value
PMT
Travel time -20% / -10% / +30%
Parking search time -25% / +25% / +50%
Fuel cost -10% / +20% / +30%
Road toll 50% / 100% / 200% of -.06 CHF/km
Parking costs -10% / +20% / +30%
Delay risk 10% / 20% / 30%
Delay time 50% / 100% / 150% of 0.1 x travel time (max. 30 min)
PT
In-vehicle time -30% / -10% / +30%
Access / egress time -50% / +/-0% / +50%
Travel costs -10% / +20% / +30%
No. of transfers -1 / +/-0 / +1
Headway -1 / +/-0 / +1 level*
Capacity utilization -1 / +/-0 / +1 level**
Delay risk 10% / 20% / 30%
Delay time 50% / 100% / 150% of 0.1 x travel time (max. 30 min)
* Headway levels: 5, 7, 10, 15, 20, 30, 60, 90, 120 min
** Level of capacity utilization: low, median, high, overload
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6. Experimental design: Route Choice
Mode Attribute Variation of empirical value
PMT
Travel time -30% / -10% / +30%
Travel costs -10% / +20% / +30%
Road toll 50% / 100% / 200% of -.06 CHF/km
PT
Main transportation mean -1 / +/-0 / +1 level
In-vehicle time -30% / -10% / +30%
Access / egress time -50% / +/-0% / +50%
Travel costs -10% / +20% / +30%
No. of transfers -1 / +/-0 / +1
Transfer time -50% / +/-0% / +50%
Headway -1 / +/-0 / +1 Stufe
Capacity utilization -1 / +/-0 / +1 Stufe
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7. Response burden and response rate
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8. Spatial differentiation: 3-level typology
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9. Spatial differentiation: Model results
• Base model with trip purpose specific attributes: 112 parameters;
• Spatial differentiation between urban, suburban, rural: 3 * 112
parameters;
• Differentiation cannot be rejected: Likelihood Ratio Test = 1093
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10. Spatial differentiation: Selected results
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Parameter Purpose Differences between typology levels (t-test)
Walking time work urban < rural, suburban
shopping suburban < urban
Costs work rural < suburban, urban
education suburban < urban < rural
shopping urban < rural
business suburban < rural
PMT travel time work rural < suburban < urban
education suburban, urban < rural
leisure rural, urban < suburban
PMT parking costs work rural < urban, suburban
education urban < rural < suburban
PT in-vehicle time work, education, shopping, business, leisure
PT headway work, education, shopping, business, leisure
Bike travel time shopping, business

11. Stability of travel behavior over time
• Comparison of valuation in demand indicators
• How stable are e.g. the VTTS over time?
• Data:
• MTMC & SP 2010: 52’208 choices (3’605 respondents)
• MTMC & SP 2015: 67’365 choices (6’099 respondents)
• Pooled estimation across 4 experiments
• RP mode choice & SP mode choice
• SP route choice (PMT)
• SP route choice (PT)
 3 scale parameters to estimate (RP as reference)
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12. Stability of travel behavior: Average valuation indicators
SP 2010 SP 2015
Travel time car (CHF/h) 16.3 16.5
Travel time PT (CHF/h) 12.6 11.6
Travel time bike (CHF/h) 41.5* 19.6*
Travel time walk (CHF/h) 46.8 18.0
Transfers PT (CHF/1 x) 2.0 1.0
Transfers PT (min./1 x) 9.7 5.1
Parking search time (CHF/h) 22.2 23.4
Frequency PT (CHF/h) 4.9 3.0
Waiting time PT (CHF/h) 4.6 11.2
Access time PT (CHF/h) 5.6 13.9
Fat: Difference significant at 1% level.
Italic*: Difference significant at 5% level.
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13. Stability of travel behavior: Valuation by distance
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14. Conclusions
Advantages of connecting MTMC and SP experiments:
• employ familiar and realistic situations;
• Get RP/SP data: Enough variation for parameter identification;
• Survey representative information.
 Data allow consideration of differences between spatial typologies and
are a reliable source for transport modeling.
Data from surveys 2010 and 2015 can be pooled.
• Most indicators show a constant behavior;
• Differences result from changes in survey method and sample.
 Keep method details constant when considering data pooling.
Future work:
• Topics of SP-study 2020: departure time choice; park and ride;
mobility pricing