GERMAN NATIONAL TRAVEL SURVEY 'MID 2016 – MOBILITY IN
GERMANY': NEW CHALLENGES – NEW APPROACHES
Angelika Schulz, Claudia Nobis
German Aerospace Center (DLR) – Institute of Transport Research
Johannes Eggs
infas – Institut für angewandte Sozialwissenschaften
Marcus Bäumer
IVT Research
1.
BRIEF HISTORY OF NATIONAL TRAVEL SURVEYS IN GERMANY
Following the long tradition of nationwide household travel surveys in Germany beginning in the mid-1970s (KONTIV 1976, 1982, 1989; MiD 2002,
2008), a new survey will be conducted from June 2016 to June 2017 on behalf of the German Federal Ministry of Transport (infas n. d.). The main objective of these repeated cross-sectional surveys is to provide representative and
reliable information on the country's everyday mobility: Individual one-day trip
diaries will be collected along with socio-demographic characteristics of private households as well as individual characteristics that are likely to influence
actual mobility behaviour (such as household composition in terms of age and
gender, car ownership, driving licence holding or individual de facto car availability).
KONTIV: Kontinuierliche Erhebungen zum Verkehrsverhalten (Continuous Surveys on Travel Behaviour)
The KONTIV surveys (Sozialforschung Brög n. d., Socialdata n. d., EMNID
n. d., Kloas & Kunert 1993) addressed the German-speaking residential population above the age of 10 (1976, 1982) or 6 (1989). Sampling was based on
address lists (1976, 1982) and random-route procedures (1982, 1989), the net
sample size was about 40,000 individuals. Data was collected via self-administered written questionnaires (PAPI: Paper and Pencil Interview), survey materials were sent by mail (1976, 1982) or were delivered and collected by interviewers (1989). The reporting period initially comprised 2-3 days (1976),
but was reduced to 1 day (1982, 1989).
MiD 2002 and MiD 2008: Mobilität in Deutschland (Mobility in Germany)
In 2002, the survey underwent a comprehensive design revision based on an
extensive methodological study (infas & DIW 2001, Kunert, Kloas & Kuhfeld
2002) that reviewed characteristics of previous European and US American
surveys similar to KONTIV. Key elements were retained, such as random
sampling of households, the coverage of an entire year (including weekends
and public holidays), the limitation on a single individual reporting day, and the
additional collection of sociodemographic characteristics and further informa© AET 2016 and contributors
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tion relevant for mobility behaviour. However, several changes were made (infas & DIW 2003): Instead of address lists or random-route procedures, population registers were used as sampling frame and children of all ages were included as well as foreign residents. residents. The final net sample consisted
of about 25,000 households and 61,000 individuals; it therefore allows for representativeness at regional level (federal states). To a great extent, written
questionnaires were replaced by telephone interviews (CATI: Computer Assisted Telephone Interview). For children aged 10 to 13 a separate questionnaire was provided, information about younger children was collected via
proxy interviews. In order to allow for more detailed analyses of travel behaviour, the question content in particular at person level was considerably extended (e.g. accessibility of public transport, individual availability of car/bike/
transit passes, physical handicaps). Furthermore, as long-distance and regular business-related trips are likely to be underreported within one-day trip
diaries, two dedicated questionnaire modules were introduced to reduce
potential undercoverage.
In 2008, the 2002 design more or less was replicated. In addition to written
questionnaires (PAPI) and telephone interviews (CATI), households could respond via the Internet (CAWI: Computer Assisted Web Interview) to provide
household information.
2.
SURVEY DESIGN OF 'MOBILITY IN GERMANY 2016'
2.1 Overall Survey Concept
To a great extent, the methodological approach of the current survey – Mobility in Germany (MiD 2016) – corresponds to its immediate predecessors of
2002 and 2008 to ensure extensive comparability over time. Nevertheless,
several adjustments have been made to meet known challenges (e.g. difficult
recruitment, decreasing response rates) and to serve emerging data needs
(e.g. impact of electric bicycles or innovative mobility services, the importance
of non-car modes, or implications of a given spatial (infra-)structure).
Compared to previous surveys, the current net sample size has slightly been
increased to about 30,000 randomly chosen households. As before, this nationwide base sample has been considerably enlarged by several regional
samples (in total about 135,000 households), but contrary to previous surveys, microdata resulting from these additional samples will also be available
for secondary analyses by third parties. Sampling has partly been based on
population registers (as in 2002 and 2008), but has been complemented by a
mixed telephone sampling using both generated landline and mobile phone
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numbers (cf. Häder & Gabler 1998). Sampling units are individuals as representatives of households.
Corresponding to the sampling frame, selected individuals resp. households
are initially contacted by mail (population register sample) or phone call (landline and mobile phone samples) to provide general information about the survey as such and to convince households to participate. In case of positive response, survey material for the initial household interview is sent by snail mail
or e-mail. Depending on available contact information, up to 10 attempts to
get in contact and several reminders during the process of data collection are
foreseen to ensure sufficient response.
Data collection is split into two consecutive parts: During the introducing
household interview, general information about the household's composition is
gathered along with basic information for each household member and household-owned cars. The household interview is followed by individual, combined
person and trip interviews to get more detailed information about each household member as well as comprehensive trip information for a pre-defined reporting day. For both parts, different survey instruments (PAPI, CATI, CAWI)
are available in order to accommodate individual preferences towards interview techniques and therefore to maximise response rates. As before, interviews with children below the age of 10 are realised in any case as proxy
interviews; in case of older children and adults, proxy interviews are optional,
e.g. if the respective respondent is not available.
As in 2002 and 2008, collected data will be complemented by additional (spatial) information from external sources. The vast majority of questions has not
been changed, very few have been dropped, some have been added and
others have been slightly modified to meet emerging data needs (e.g. use of
car sharing or mobile devices for transportation purposes). Trip data is being
collected for individual, pre-defined single-day travel periods. The final sample, however, will cover the entire year to reflect seasonal variation as well as
differences between working days and weekends. In break from tradition, for
each trip detailed origin and destination information (either address or point of
interest) is being collected for geocoding purposes.
2.2 What is new in 2016?
Methodological changes apply at different levels such as sampling design,
survey instruments, questionnaire content, and the integration of supplementary spatial data.
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In a nutshell, the following modifications have been implemented (for more details see section 3):
• In terms of sampling a triple frame approach has been adopted consisting of
three complementing sampling frames (see section 3.1).
• Aiming at increasing overall response rates, the mixed-mode approach is extended to all parts of the survey (see section 3.2).
• The intended coverage of additional topics resulted in a set of new questions.
In order to limit the already high response burden, the catalogue of questions
has been divided into a core questionnaire and several sub-questionnaires
(see section 3.3).
• Trip diaries will include detailed origin and destination information (ideally addresses) for each trip (see section 3.4).
• Geocoded information at household and trip level (residential addresses, origins and destinations of trips), which will be conducted via questionnaires and
interviews, will further be enriched by merging additional spatial information
(e.g. given infrastructure and respective accessibility) (see section 3.4).
• In order to provide estimates for smaller spatial units (e.g. districts) or for specific sub-groups of the population, small area estimation (SAE) techniques will
be applied (see section 3.5).
3.
MODIFICATION AND INNOVATION
Given the survey's embeddedness into a widely used time series, the implementation of any change in terms of methodology and/or content always involves the risk of affecting comparability of results over time. Accordingly, any
modification, however small, is a balancing act between adopting inevitable innovation and keeping to a long tradition. In the following section, selected
areas of modification and innovation are described in more detail.
3.1 Sampling
A central task of the current survey is to provide reliable key mobility indicators not only for Germany as a whole, but also for its 16 federal states and
even smaller areas. Aiming at complex stratified analyses, sufficient net samples for intended spatial categories are essential. Therefore, the overall net
sample consists of a nationwide base sample of about 30,000 households,
presumably representing about 60,000 individual household members. Together with several regional add-on samples the final sample amounts to a
total of about 135,000 households.
For each of the randomly selected households and associated household
members, a considerable amount of data is collected, in particular trip data
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covering reporting days randomly assigned to respondents throughout an entire survey year. A final outcome, for instance, will be estimates of the total
number of trips made by the German residential population in 2016 or the total
trip length differentiated by mode of transport and trip purpose.
In order to obtain the required sample of trips, the underlying sample of
households (resp. individuals) has to be drawn at first. Usually, there are several options to generate such a sample (cf. Hautzinger 1997):
• cluster sampling based on population registers (if available at all), with spatial
entities such as cities as primary sampling units and households (represented
by individuals) as secondary sampling units, or
• sampling based on generated lists of phone numbers with either households
or individuals as sampling units (random digit dialing samples).
As it increasingly becomes difficult to obtain satisfactory response rates from
samples drawn from a single sampling frame, a triple frame approach has
been adopted consisting of three complementing sampling frames (Lohr 2007)
(Figure 1).
Figure 1 Triple frame sampling approach: overlapping and complementing
sampling frames (own illustration, based on Lohr 2007)
An 'area' frame A (a complete list of municipalities with population registers) is
used to obtain a two-stage cluster sample (with geographical regions as primary units and households resp. individuals as secondary units). It is complemented by two 'list' frames B and C. Both frames B and C are lists of generated landline (B) and mobile (C) phone numbers (again with individuals resp.
households as sampling units), providing single-stage cluster samples (phone
numbers as sampling units correspond to clusters of persons and persontrips).
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A particular methodological challenge (as compared to the 2008 survey) is to
combine these three frames for weighting and estimation purposes. In this
context, among other things, inclusion probabilities for the selected units in the
sample have to be determined appropriately at both household and person
level.
3.2 Survey Instruments
Taken as a whole, the survey is designed as household study. After drawing
the sample, however, nothing is known about the households' composition
yet. Therefore, data collection is split into two separate parts: At first, a household interview is used to gather general information about the entire household including its composition. Based on this information, complementary individual questionnaires for all household members are sent to the respective
contact address (either postal or e-mail addresses according to the households' choice made during the household interview). Material sent together
with the initial household questionnaire (PAPI) includes a cover letter inviting
households to participate as well as two leaflets with information about the
survey in general and data security issues. As an individual CAWI access
code is already provided at front of the PAPI questionnaire, households can
immediately switch to the online mode to complete the household interview if
they prefer to.
The main reason for providing different modes of data collection is to accommodate participants' individual attitudes and preferences towards certain
modes. Aiming at increasing not only mere participation rates, but also accuracy of responses and reliability of resulting data, respondents are given the
option to choose their preferred mode, either PAPI, CATI or CAWI, and even
to switch modes in the course of the survey (e.g. household interview via
CAWI, person/trip interview via CATI).
In order to minimise methodologically induced bias, great effort has been
made to develop instruments as similar to each other as possible (question
order and wording, answer categories). Both online and telephone questionnaires were pretested, the paper questionnaire as well as accompanying notification and introductory material were discussed with several focus groups.
3.3 Catalogue of survey questions
The interview programme of the household questionnaire is almost identical to
the programme of the 2008 survey. Besides socio-demographic information
for each household member (including age, sex and occupational status),
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basic information about up to three household-owned cars is collected (including car manufacturer, annual mileage, year of construction, and type of propulsion). Further questions refer to driving licences, membership of car
sharing services and household income. Half of the base sample receives
additional car-related questions (e.g. engine power, actual parking conditions).
The person/trip questionnaire consists of a core module and several sub-modules: The core module applies to all respondents, sub-modules are assigned
to sub-samples immediately after sampling. While each individual of the base
sample receives a set of two randomly selected sub-modules, individuals of
regional samples receive a set according to preferences of the respective regional sponsor.
The set of core questions is composed in such a way that comparability of important items is guaranteed throughout the time series (MiD 2002, 2008,
2016). Among others, the first part comprises questions related to the individual availability of cars or electric bicycles, the common use of different means
of transportation, and the reporting day. The core module is complemented
with four sub-modules, each of them assigned to 25 % of the total sample.
Questions are related to (1) cycling and short-range mobility, (2) satisfaction
with infrastructure and the transport system, (3) means of transportation and
shopping, and (4) individual characteristics relevant for mobility behaviour. An
additional module on long-distance travel is assigned to 50 % of the base
sample.
The modular system was chosen to reduce respondent burden by reducing
interview time. As modules are randomly assigned to the gross-sample, unbiased estimates can be calculated for the country.
The second part of the person/trip interview covers the individual reporting
day in terms of trips. The range of collected information is almost identical to
the 2008 survey (e.g. trip purpose, distance, means of transport, departure
and arrival time). As in 2008, information about regular job-related trips is collected in aggregated format (e.g. total number and total mileage of trips made
during working time). In contrast to 2008, for each private trip, details of both
origin and destination are collected; respondents are asked to provide either
addresses or points of interest (such as shops, train stations, churches etc.).
3.4 Geocoding and Data Enrichment
It is foreseen to geocode each residential location (households' addresses) as
well as both origin and destination of each reported trip (addresses or points
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of interest (POI)). In terms of resolution, geocoding will be conducted as precise as possible, ideally up to an accuracy level of house numbers. In order to
minimise response burden during trip recording, trip data collection and validation will be supported by an extensive online database containing several
million POI, which are already geocoded. For the addresses, the available address information will be transformed into Gauss-Krüger coordinates. For data
protection reasons, precise geocodes will be allocated to a spatial raster grid
of squares in order to prevent re-identification of households. Depending on
population density, squares will vary in size.
Geographic coordinates are used for further enrichment of genuine survey
data. It therefore reduces respondent burden, as crucial information can be
obtained without asking survey participants.
Data enrichment will take place both at individual resp. household level and at
area level. Based on the household's residential address, several additional
indicators will be calculated, e.g. the actual distance to the nearest provider of
non-durable consumer goods, to educational institutions, bus stops, train stations, or highway access points. Furthermore, for a sub-sample an ex-post
routing of reported trips will be performed using geocoded origins and destinations of the respective trips. For trips made by car or by public transport, alternative travel times will be estimated (i.e. for car trips the respective travel
time by public transport and vice versa).
Additionally, characteristics of the households' residential areas will be complemented (e.g. population density or the type of residential area). Such information will facilitate further in-depth analyses (e.g. prediction of mode choice
behaviour or travel patterns).
3.5 Small Area Estimation (SAE)
Given the nationwide approach and the stratified sampling design, MiD microdata so far has mainly been used to estimate population characteristics at national or state level and for respective spatial categories. In order to provide
estimates for smaller spatial units (e.g. districts) or for specific sub-groups of
the population, small area estimation (SAE) techniques will be applied.
Small-scale passenger transport and mobility indicators usually refer to narrowly defined spatial aggregates of persons or households. Typical key indicators are, for example, the 'overall total number of trips made by the inhabitants
of administrative district X in 2016' ('area-specific passenger trip volume') or
the 'total number of trips made by the inhabitants of administrative district X in
2016 split by mode of transport'. It is often impossible to produce reliable
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'direct' estimates for small-area mobility indicators because frequently the
area-specific sample sizes are too small, and hence, estimates have unacceptably large standard errors; in extreme cases the sample size may even
be zero in some of the geographical areas of interest.
Figure 2 Finding more accurate estimates for a given area by 'borrowing
strength' from related areas (own illustration)
Recent (explicit) small area models typically are based on so-called mixed linear models containing both fixed and random effects (see, for instance, Rao &
Molina 2015). In particular, the latter accounts for the effect of any area characteristic not included in the statistical model. Depending on the availability of
data concerning the auxiliary variables, either area level or unit level models
are used for estimation purposes.
4.
IMPLICATIONS OF METHODOLOGICAL CHANGES
Modifications as described in the previous section will have consequences for
further steps throughout the entire survey process.
In particular the complex sampling design – triple frame approach in combination with national base sample and regional add-ons – as well as the questionnaire's segmentation into a core segment addressed to all respondents
and several theme modules addressed to sub-samples only, will clearly affect
the weighting process. Known challenges are multiple sampling frames, resulting in complex inclusion probabilities of sampling units (households, individuals).
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The mixed-mode approach is particular challenging, as at least two or more
different instruments (= questionnaires) have to be consistently designed.
During the design process, particularities and potential restrictions of each
type of instrument have to be taken into account. The traditional paper-andpencil questionnaire (PAPI) clearly is the most limited mode in terms of length
(number of questions and answer choices, extent of instructions) and complexity (wording, underlying concepts), as respondents have to get along without any direct interaction, support and motivation. Computer-aided instruments such as CATI and CAWI on the other hand entail several advantages
compared to their analogue counterpart: Elaborated filter techniques based on
individual characteristics and previous answers facilitate more tailored questions. Entry of raw data already is completed by the end of the interview,
measures of input control, aiming to avoid the entry of invalid or inconsistent
values can be applied throughout the entire interview. However, each instrument will not only contain its own specific set of questions and resulting variables, but will also produce a unique set of answer and missing codes. Moreover, a certain share of missing values will result from the questionnaire's segmentation: Theme modules are randomly assigned to sub-samples already
during the initial sampling process, regardless individual reasonableness (e.g.
additional questions related to household-owned cars may be addressed to
households without any own car). Accordingly, such questions may not properly apply, therefore resulting in qualified missing values. At the end, all these
complementary variables and coding schemes have to be integrated afterwards during data processing. In order to reduce missing values, it is intended
to use imputation techniques. Depending on the final pattern of missing response, the respective variables' level of measurement and the availability of
suitable variables for estimation purposes, missing information will be imputed
using linear or logistic regression models.
Given the complex survey design and the complex data structure, data postprocessing (including weighting, coding, cleaning and imputation procedures)
has to be properly documented, as final data will be publicly available for secondary analyses. Contrary to previous MiD surveys, both samples (base and
regional) will be provided. The final set of microdata and basic results are expected for early 2018.
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infas (Institut für angewandte Sozialwissenschaft) & DIW (Deutsches Institut
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Lohr, S. (2007): Recent Developments in Multiple Frame Surveys. Proceedings of the Survey Research Methods Section, American Statistical Association (ASA). 3257-3264.
Rao, J. N. K. & Molina, I. (2015): Small Area Estimation, 2. Edition, Wiley,
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Further reading
Barnett, V. (1991): Sample Survey Principles and Methods. 3. Edition, Edward
Arnold, London.
Cochran, W. G. (1977): Sampling Techniques. 3. Edition, Wiley, New York.
Foreman, E. K. (1991): Survey Sampling Principles, Marcel Dekker, New
York.
Holz-Rau, Chr. & Scheiner, J. (2006): Die KONTIVs im Zeitvergleich. Möglichkeiten und Schwierigkeiten beim Vergleich der Erhebungswellen. Internationales Verkehrswesen, 58 (11), 519-525.
infas (Institut für angewandte Sozialwissenschaft) & DIW (Deutsches Institut
für Wirtschaftsforschung) (2004): Mobilität in Deutschland. Ergebnisbericht.
Project no. 70.0736/2003 (Bundesministerium für Verkehr, Bau- und Wohnungswesen, BMVBW).
Internet resource (last access July 15, 2016): http://www.mobilitaet-indeutschland.de/pdf/ergebnisbericht_mid_ende_144_punkte.pdf.
Mukhopadhyay, P. K. & McDowell, A. (2011): Small Area Estimation for Survey Data Analysis Using SAS Software, SAS Global Forum 2011, Paper 3362011.
Rao, J. N. K. (2003): Small Area Estimation, Wiley, New York.
Vaish, A. K., Chen, S. et al (2010): Small area estimates of daily person-miles
of travel: 2001 National Household Transportation Survey. Transportation,
Vol. 37, Issue 6, pp. 825-848.
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