All time references are in CEST
Current Developments in Mobility Survey Methods |
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| Session Organisers | Dr Johannes Eggs (infas) Dr Stefan Hubrich (TU Dresden) Dr Rico Wittwer (TU Dresden) |
| Time | Tuesday 18 July, 09:00 - 10:30 |
| Room | |
Travel surveys collect data on the mobility of populations. Large-scale national household travel surveys are used to estimate key mobility figures on national or sub-national levels, like out of home rates, trips and transport mode rates, and to predict the demand of the population regarding the use of transportation modes. But high respondent burden and sinking response rates are pressing on the established travel survey designs. On the other hand new technologies are available to ease survey participation.
The focus of this session is explicitly wide and aims to gather an overview about the cur-rent developments, challenges and ideas for mobility surveys. Papers matching one of the following aspects are invited to be part of this session (but are not restricted to):
– New technologies for data collection and mixes mode approaches
– Integrating GPS data in trip and survey data
– Gamification in travel surveys
– Survey mode effects in travel survey
– Improving trip reporting in different modes
– Improving household completion rates
– Effects of proxy interviews
– Methods to collect mobility data for specific sociodemographic groups (e.g.children)
– New sample frames and weighting procedures
– Incorporation of big data
Keywords: Survey methods, Survey design, travel, traffic, mobility
Ms Lisa Eyers (Department for Transport) - Presenting Author
Since 1965, England's National Travel Survey (NTS) has collected data from households on their personal travel over a 7 day period using a paper travel diary. The paper diary represents significant printing, logistical and processing costs, adding to the time it takes to process and release NTS data.
In 2018, the Department for Transport commissioned a digital specialist contractor to develop a web-based digital diary to be hosted on the UK government's gov.uk platform. This represents the biggest methodological change to this survey in many years. Digital development has taken place alongside a programme of work to integrate the digital diary into the long-established data collection process which utilises face-to-face interviews in the household.
A large-scale test was conducted in early 2024 to assess the impact of introducing a digital-first approach. The Parallel Run compared response rates, sample composition, parameter estimates and fieldwork procedures between standard paper diary respondents and those offered the digital diary. The test found that the digital diary collects robust data and is integrated well, having no substantial impact on response rates, fieldwork, or processing procedures. Trip data was found to have some statistically significant but small differences, with some indications of under-reporting in the digital condition. Certain socio-demographic factors were systematically related to digital uptake (e.g., age and ethnicity) and proxying rates (e.g., sex, disability, and education).
Dr Stefan Hubrich (TUD Dresden University of Technology) - Presenting Author
The household travel survey (HTS) “Mobility in Cities – SrV” has been conducted in 2023 for the 12th time since its foundation in 1972. This comprehensive study surveyed over 280,000 individuals across approximately 500 German municipalities to analyze their travel behavior. SrV 2023 was a year-long, cross-sectional HTS, utilizing municipality-specific stratified random samples drawn from the population registers of the participating municipalities. Each selected individual was asked to provide details about their household, household members, and all trips made during a single day. Data was collected through telephone interviews or online questionnaires. This contribution provides an overview of the organizational and methodological framework of the survey, followed by insights into fieldwork experiences (i.e. response rates, selectivity), and discusses key survey results as well as current trends of travel behavior in Germany.
Dr Eggs Johannes (infas) - Presenting Author
In this paper, we analyze how the number of reminders influences the response rate on the first stage of a two stage household travel survey and whether this might negatively impact key measures of the survey.
The German household travel survey 2024 will include interviews with approximately 400,000 persons living in about 200,000 households.
The survey consists of two consecutive parts: An introducing household interview is designed to collect basic information about the household, its members, and the respective car fleet. It is complemented by individual person/trip interviews to collect more detailed information about each household member as well as detailed trip information for the respective travel day. The personal interview is delayed by approximately 10 days.
During the survey the number of reminders on Household level was increased from two to three reminders. As the travel day and thus the field time of each household was randomly distributed over the gross sample, it is also randomly assigned whether a household will receive two or three reminders.
In this paper we will answer the following research questions:
a.) How much does the additional reminder increases the response rate on household level?
b.) How much does the additional reminder increases the response rate on person level?
c.) Does an additional reminder has a negative effect on key variables like number of reported trips and trip details?
Dr Rico Wittwer (TUD Dresden University of Technology) - Presenting Author
Household Travel Surveys (HTS) have a long tradition and are conducted regularly in many countries. This contribution focuses on two large-scale, repeated cross-sectional surveys from Germany. These nationwide NHTSs and local HTSs provide extensive data on daily mobility patterns, socio-demographic characteristics, household structures, access to mobility options and routines, as well as spatial and neighborhood-related variables. The complexity and scope of these surveys present significant challenges for data collection, processing and analysis. This contribution aims to address some of the methodological challenges in travel behavior research, such as data merging, accounting for the hierarchical data structure, and deciding whether to use weights in explanatory statistical models. Therefore, we discuss the effectiveness of data harmonization efforts to enhance the usability of combined surveys for transport planning practice. Furthermore, we will analyze the impact of hierarchical data structures on statistics through model comparison, evaluating the performance of (Generalized) Linear Mixed Models ((G)LMMs) and addressing the question of how much variance can be explained by different levels. In addition, we will discuss the use of weights in statistical explanatory models. As expected, the combination of harmonized and merged data analyzed using (G)LMMs reveals complex relationships and dependencies within the data, leading to deeper insights into travel behavior.
Mr Hauke Reckermann (RheinMain University of Applied Sciences) - Presenting Author
Professor Matthias Kowald (RheinMain University of Applied Sciences)
Professor Christine Eisenmann (Brandenburg Technical University (BTU) Cottbus-Senftenberg)
Dr Ilka Dubernet (Institute of Transport Research, German Aerospace Center (DLR))
Empirical data from surveys, observations and traffic counts are of importance to transport planning. This data is key for essential transport planning tasks, e.g. decisions on infrastructure, transport services, and revenue sharing. Therefore, empirical data needs to be reliable and robust. Furthermore, transparency, reproducibility, and availability are important issues.
Recently, an active and growing field of transport surveys and methods has emerged, including new methods of electronic data collection and observation. This development results in uncertainties and a constant discussion of the advantages and disadvantages of emerging data sources and methods in terms of new insights and an approximation to the "ground truth" of actual transport situations and behaviour. Due to the rapid development, it is challenging for transport planning practitioners to keep up to date with the current state of art on empirical methods. Fostering knowledge transfer and providing overviews of new developments is one goal of the German Road and Transportation Research Association (FGSV), which publishes the “Recommendation for Empirical Methods in Transport” (EVE). Due to many changes in the field of empirical methods in transport, the EVE is currently being revised. The most challenging question is, which traditional and new methods should be recommended in the guideline and for transport planning practice. The recommendations have to be robust and reliable as they will last for several years or even one or two decades.
Contribution of the paper: We aim to discuss the structure and contemplated content of the new EVE with the ESRA community. This includes new empirical methods, methods of participation and civil sciences, as well as issues of data protection and documentation. Furthermore, we aim to discuss transfer paths from academia into transport planning practices. Feedback from the international community is highly welcome to learn from new methods and similar guidelines around the globe.
Mr Nicolas Salbach (Karlsruhe Institute of Technology - Institute for Transport Studies) - Presenting Author
Ms Miriam Magdolen (Karlsruhe Institute of Technology - Institute for Transport Studies)
Mr Lukas Burger (Karlsruhe Institute of Technology - Institute for Transport Studies)
Professor Peter Vortisch (Karlsruhe Institute of Technology - Institute for Transport Studies)
Surveys often face challenges like low response rates, high dropout rates, and incomplete data, complicating analysis. Missing data may require imputation, introducing uncertainties, while high dropout rates increase costs and limit scalability. Developing strategies to ensure high data quality and participation is essential.
Our study examined whether returning survey results could enhance respondents’ motivation and data quality, specifically by returning their assigned urban mobility type. The case study was conducted in Dreimühlenviertel, a small, central district in Munich, Germany. The survey work consisted of two parts: first, a travel skeleton survey collecting typical travel behavior, including everyday and long-distance travel, and attitudes towards transport modes (von Behren et al., 2018). Magdolen et al. (2019) segmented respondents into urban mobility types, allowing classification of respondents in subsequent surveys, including those in Dreimühlenviertel. In a customized follow-up survey months later, respondents were asked to self-classify into these urban mobility types. Afterwards, they received feedback in the form of a characterization of their urban mobility type based on their travel skeleton survey report. The study aimed to evaluate incentivizing respondents with personalized results, refine urban mobility types, and assess their clarity and communicability.
Our approach achieved a 46% response rate in the follow-up survey with only two incomplete responses (N = 53). While we cannot separate the effect of feedback from respondents’ intrinsic motivation, the results suggest that informing respondents about their classification outcomes improves data quality and participation. This approach is effective in segmentation studies, where group memberships are easy to communicate. Despite a four-month lag between surveys, respondents remained interested in their results. Sharing results immediately could further boost engagement and satisfaction.
Our study suggests that incentivizing respondents with their classification results can boost motivation, enhance data quality, and increase response rates, ensuring reliable findings.
Ms Danielle Remmerswaal (Utrecht University) - Presenting Author
Dr Jonas Klingwort (Statistics Netherlands)
Professor Barry Schouten (Statistics Netherlands)
Given the limitations of traditional diary studies (measurement errors and nonresponse) and the increased use of smartphones, growing attention is given to smartphone-based travel surveys. Smartphone travel apps can make use of geolocation sensors present on the smartphone to construct a travel diary.
Statistics Netherlands developed a state-of-the-art smartphone app to measure mobility behavior in the Netherlands. The smartphone app automatically compiles a travel diary, consisting of a series of movements and stops, based on passively collected geolocation data. In 2022 and 2023, the app was tested in a large-scale field test (initial sample n=3200, realized sample n = 505). This test aimed to analyze and evaluate design choices such as the data collection strategy (response rate and participation duration), the app sensor tracking implementation (measurement frequency and battery management), the respondent interaction (active-passive trade-offs), the data quality and validation (quality requirements), and the AI-ML predictions (travel mode/motive prediction).
The field test included several randomizations to study these design choices, which we will report on in this paper. In particular, we address an influential choice: the duration of the travel diary. For this choice we look at data quantity and data quality as a function of time in-study. Our conclusions and recommendations have relevance across a wide range of research areas concerning studies on (time-use) behaviour, studies considering similar design choices, and studies using (smartphone) sensors.