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Post-Car World: data collection methods and response behavior in a multi-stage travel survey

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Abstract

The main research question addressed by this study is to what degree individuals would change travel modes, time allocation and activity patterns after experiencing large changes in generalized transportation costs and how they would react regarding their longer-term ownership in mobility tools, assessing suppressed demand effects from an activity-based perspective. The empirical basis is a multi-day travel and online diary that is required to obtain the personalized reference values for the later stated choice and stated adaptation tasks. This paper provides first detailed information of the survey methods, recruitment and fieldwork. An initial investigation of the data and its quality attributes, descriptions of the sampling structure and response behavior are presented. Participation choice models indicate that a high incentive level leads to a higher participation rate, but the net-effect on completing the survey is zero: once recruited, higher incentives also lead to a higher drop-out incidence. Certain socioeconomic characteristics are consistently overrepresented in the sample: season ticket ownership, better education and higher income strongly increase participation and completion of the survey. Findings reveal saliency effects, whereby response behavior is influenced by the respondents’ interest in the survey topic. While general fatigue effects can only be detected for the number of reported online activities, better educated and car-less respondents exhibit an increased reporting behavior of trips over time. Importantly, while showing no effects on completion of the survey, higher incentives tend to increase response quality in terms of absolute levels (trips) and stability (online activities).

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Notes

  1. Project website: http://postcarworld.epfl.ch/

  2. Institute website: http://www.ivt.ethz.ch/en/institute/vpl.html

  3. Note that in the pre-test, we asked for a 2-week reporting period, which, given the very large administrative effort and response burden, was reduced to a 1-week reporting period in the main survey.

  4. Note that point-scores for the travel and planning diary are based on an average of 22 trips per week. Respondents could report a maximum of 40 trips per week.

  5. Many respondents were complaining about the work load, and were facing conceptual problems with the prospective trip planning task for the second reporting week.

  6. Note that the taxi alternative was excluded in the main survey as it was only chosen by one respondent in one choice situation.

  7. This category also exhibits the highest E-shop** market share in Switzerland (Rudolph et al. 2015).

  8. Durable goods expenditures, including standard electronic appliances, were part of a separate questionnaire on an aggregated yearly basis (see also Table 2) and not used for reference value calculation. If a respondent did not report any shop** trip during the multi-day reporting period, a potential shop** location was chosen offering a high variety of goods and high level of accessibility, assigning this respondent to the standard electronic appliances experiment as from a behavioral aspect it might be more problematic to postulate a travel distance to a grocery store. In addition, reference travel time and travel cost to the store were calculated for either carsharing/carpooling or public transportation. To avoid anchoring effects with respect to transportation modes, a specific mode for the in-store alternative was never explicitly mentioned.

  9. Note that the route choice experiments were not included in the pre-test.

  10. While asking at least one household member to conduct both interviews, in some cases multiple household members were willing to conduct the first SA experiment.

  11. This number is referring to the conducted interviews (households that were eligible for the payment of the incentive). Note that because of technical problems with the first SA tool, some households had to be excluded from the final data set. The number of valid respondent/household observations are presented for each tool separately in “Tool I: adaptations in daily scheduling” and “Tool II: adaptations in mobility tool ownership” sections. Also note that tool II (adaptations in mobility tool ownership) was not yet available in the pre-test and that in wave III, no interviews were conducted anymore as the survey budget was exhausted.

  12. Touring Club Schweiz: https://www.tcs.ch/

  13. This solves the boundedness problem of the original dependent variable (the probability to participate in a survey).

  14. See also Table 1: given average survey response durations between three (wave III) and 6 h (pre-test), the response rate was always above 52%. Note that in the pre-test, many respondents reported a general discontent regarding the high response burden, especially for stage I of the survey: while the socioeconomic questionnaires and the travel diary (although exhibiting a high response burden) worked well, data quality and response behavior of the trip planning and expenditure questionnaires were suffering. While some respondents did not understand the purpose of the trip planning task, others were overwhelmed by calculating their long-run expenditures for the different categories (communication, housing, education, etc.). To reduce the response burden in the main survey, a natural consequence was to skip and simplify some of the questionnaires to achieve a higher data quality of the remaining tasks and to reduce drop-out incidence.

  15. To compare with the PCW sample, only a subsample of the MZ2010 is considered, limited to the Canton of Zurich.

  16. Low education: no education, obligatory school, lower commercial school or apprenticeship. Medium education: grammar school, higher education entrance qualification, proficient diploma or professional school. High education: higher technical academy, college or university.

  17. A major problem involved the recruitment of all eligible (older than 18 years) household members, simultaneously affecting the age distribution in the PCW sample: Although larger households are overrepresented, mostly fractions (e.g. parents or the addressed household heads) of all eligible household members actually participated in the survey.

  18. Note that for model estimation, the medium incentive categories (70 CHF and 80 CHF) were pooled together, as their effects were not significantly different from each other.

  19. Note that these numbers are smaller than the ones reported in Table 1, as the model in Table 13 only includes households who also reported their income.

  20. Arguing that the Heckman estimator in such a case is problematic, Sartori (2003) derives an estimator under the assumption that the error terms in both equations are identical, which, in the traditional Heckman approach, is estimated from the data. In the current application, however, both approaches yield exactly the same results.

  21. A slightly different interpretation is that high incentives might convince people who are actually not interested in the survey topic to participate, but when realizing the enormous response burden, they decide to drop-out.

  22. Note that four respondents are excluded for analyzing the number of online activities. They were classified as complete, but did not fill in the online diary.

  23. Due to the relatively low number of (independent) observations for which the incentive levels were varied (56 respondents), results have to be treated with caution.

Abbreviations

AICc:

For finite sample size corrected Akaike Information Criterion

CHF:

Swiss Francs (1 CHF \(\approx\) 1 US$)

CP:

Carpooling

CS:

Carsharing

IVT:

Institute for Transport Planning and Systems at ETH Zurich, Switzerland

MIV:

Motorized individual vehicles (car, motorbike)

MPV:

Motorized public vehicles (carsharing, carpooling, taxi)

MZ2010:

Data from the (representative) Swiss microcensus for mobility and travel behavior

PCW:

Post-Car World (name of the project)

PT:

Public transportation (train, bus)

RP:

Revealed preference

SA:

Stated adaptation

SP:

Stated preference

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Acknowledgements

The authors gratefully thank to the SNSF (Swiss National Science Foundation) for funding the Post-Car World project (Grant Number 2-77894-13). We also give thanks to Simon Schmutz, former research assistant at the IVT, for his outstanding contributions to the project.

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  1. Institute for Transport Planning and Systems (IVT), ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland

    Basil Schmid, Milos Balac & Kay W. Axhausen

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  1. Basil Schmid
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Correspondence to Basil Schmid.

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Appendix

Appendix

See Table 16 and Figs. 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39.

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Household questionnaire I

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Household questionnaire II

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Vehicle questionnaire I

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Vehicle questionnaire II

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Person questionnaire I

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Person questionnaire II

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Person questionnaire III

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Person questionnaire IIII

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Travel diary I

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Travel diary II

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Travel diary III

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Travel diary IIII

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Online diary I

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Online diary II

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Short-term expenditures

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Long-term expenditures

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Mode choice SP introduction

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Example choice situation: mode choice SP

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Table 16 Assignment of the different mode choice SP questionnaire types
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In-store versus online shop** SP introduction

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Example choice situations: in-store versus online shop** SP

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Route choice SP introduction

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Example choice situations: in-store versus online shop** SP

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Attitudinal questionnaire I

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Attitudinal questionnaire II

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Attitudinal questionnaire III

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Attitudinal questionnaire IIII

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Attitudinal questionnaire V

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Attitudinal questionnaire VI

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Attitudinal questionnaire VII

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Attitudinal questionnaire VIII

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Example choice situation: adaptations in daily scheduling (tool I)

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Mobile persons in household: adaptations in mobility tool ownership (tool II)

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Vehicle information: adaptations in mobility tool ownership (tool II)

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Example choice situation (base scenario): adaptations in mobility tool ownership (tool II)

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Schmid, B., Balac, M. & Axhausen, K.W. Post-Car World: data collection methods and response behavior in a multi-stage travel survey. Transportation 46, 425–492 (2019). https://doi.org/10.1007/s11116-018-9968-2

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