Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals

Abstract

Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. There is extensive evidence for an increasing amount of mobility data, availability of new data sources and potential usage scenarios for such data. However, little is known about the current use of these data in policy making, planning and related fields. To the best of our knowledge, it has not been investigated yet to which degree professionals in the broader field of cycling promotion benefit from an increasing amount of cycling-related data. Thus, we conducted a multi-lingual online survey among domain professionals and acquired data on their perspectives on current data availability, use and suitability as well as the potential they see for the use of cycling data in the future. In total, we received 325 complete responses from 32 countries, with the vast majority of 241 valid responses originating from Germany, Austria and Italy. Key findings are: 84% of domain professionals attribute high importance to data, and 89% state that they currently cannot or only partly solve their tasks with the data available to them. Results emphasize the need for making more and better suited data available to professionals in cycling-related positions, in both the private and public sector.

Dataset:https://doi.org/10.5281/zenodo.5705609.

Dataset License: CC-BY 4.0

1. Background and Motivation

Awareness of the role of sustainable mobility with regard to climate goals and livability is constantly growing. However, the potential of walking and cycling cannot be fully unlocked in many cases, due to car-centric urban planning and infrastructure design. In order to make sustainable mobility more visible and to provide a sound evidence base for decision and planning processes, data on cycling and walking mobility are of crucial importance.

Various technological developments such as advances in sensor technology, internet of things and smart devices have enabled opportunities for mobile data acquisition. Paired with an increased uptake and acceptance of these technologies in society and their frequent application by a large number of users, this led to growing amounts of data being generated. Currently, no weakening of this trend has been observed, thus resulting in data volumes not manageable by conventional means [1]. New opportunities, driven by technological advancements, are widely anticipated in the context of smart cities, transport research and urban mobility [2,3,4,5]. Following the growing amount of mobility data being collected by various stakeholders, high expectations are attributed to their potential for policy makers and planners in the context of active mobility, especially cycling. Lee and Sener recently reviewed traditional and emerging data sources for cycling and walking mobility and, in addition to generally increasing data availability, found several key challenges related to validity, sampling bias, privacy, lack of contextual information and data accessibility [6].

However, in contrast to this growing amount of data on cycling, Steenberghen et al. found that 60% of surveyed representatives of responsible national authorities from EU member states, Norway and Switzerland were not able to provide the average annual distance cycled per inhabitant at the national level [7]. Against the backdrop of a frequently cited data deluge [8], these findings appear counter-intuitive, especially when the high level of aggregation is taken into account. We thus hypothesize that although there exist vast amounts of mobility data, these data are not enough, not appropriate or not accessible to those cycling professionals who would require it for their daily tasks. To our knowledge, no previous research quantitatively estimated the gap between data demand and opportunities offered through data currently available to domain professionals. To fill this gap, we designed and conducted a multilingual online survey in summer 2020 that was distributed via professional networks in the domain of cycling mobility. The investigation of current and future data use and demand emerged as part of the research project “Bicycle Observatory” (https://bicycle-observatory.zgis.at, accessed on 30 September 2021), which aimed at fusing technical sensor data (such as trajectories and counting data) with social science data (e.g., from interviews and questionnaires) for deriving a multi-dimensional, spatially differentiated picture of cycling mobility [9].

2. Data Description

The presented dataset contains all completed responses to the online survey conducted in the period from 23 June 2020 to 31 August 2020. A total of 568 unique site visitors were registered, out of which 325 completed the survey (57%). We presume the high dropout rate resulted from the narrow definition of our target group and accordingly expert-oriented formulation of questions.

2.1. Survey Structure, Content and Design

The survey consisted of four sections, each focusing on a different aspect: (1) the individual (professional) background of respondents, (2) their current use of cycling data, (3) assessment of their current data use and (4) respondents’ wishes regarding future data access and use. A full reference of survey questions and pre-defined multiple-choice options is provided in Appendix B. In

Table A1. Description of dataset fields.

Section	Question No.	Question Type	Field ID	Datatype
Metadata	-	-	response_id	Integer
Metadata	-	-	date_submitted	Date and Time [DD.MM.YYYY HH:mm]
Metadata	-	-	start_language	String
1	1 *	single choice options + other	field_of_work	String (defined items)
1	1 *	single choice options + other	field_of_work_other	String (free text)
1	2 *	single choice options	geo_scope	String (defined items)
1	3 *	single choice options	country	String (defined items)
1	4 *	single choice options	gender	String (defined items)
1	5 *	value input	year_of_birth	Integer
2	6 *	slider (0–100)	data_importance	Integer [%]
2	7 *	multiple choice + other	current_source_none	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_rep_mobility _surveys	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_manual_counts	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_automated _counts	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_surveys	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_feedback_app	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_app_traj	String/Boolean [Yes/No]
2	7 *	multiple choice + other	current_source_other	String (free text)
2	8 *	multiple choice + other	current_task_none	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_infra_planning	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_measure_eval	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_particip	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_monitoring	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_communication	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_research	String/Boolean [Yes/No]
2	8 *	multiple choice + other	current_task_other	String (free text)
3	9 *	single choice options	as_data_appropriate	String (defined items)
3	10 *	single choice options	as_know_share	String/Boolean [Yes/No]
3	11	numeric input	as_num_share	Float [%]
3	12 *	single choice options	as_know_trip_purpose	String/Boolean [Yes/No]
3	13 *	single choice options + other	as_main_trip_purpose	String (defined items)
3	13 *	single choice options + other	as_main_trip_purpose_other	String (free text)
3	14 *	single choice options	as_konw_trip_length	String/Boolean [Yes/No]
3	15	numeric input	as_num_trip_length	String (free text)
3	15	[derived from as_num_trip_length]	as_num_trip_length_corr	Integer [meters]
3	16 *	single choice options + other	as_main_motivation	String (defined items)
3	16 *	single choice options + other	as_main_motivation_other	String (free text)
4	17 *	multiple choice + other	future_source_none	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_rep_mobility_ surveys	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_manual_counts	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_automated_counts	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_surveys	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_feedback_app	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_app_traj	String/Boolean [Yes/No]
4	17 *	multiple choice + other	future_source_other	String (free text)
4	18 *	multiple choice + other	future_task_none	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_infra_planning	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_measure_eval	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_particip	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_monitoring	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_communication	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_research	String/Boolean [Yes/No]
4	18 *	multiple choice + other	future_task_other	String (free text)
Remarks	19	text input	remarks	String (free text)
4	17 *	[derived columns]	future_source_corr_<name>	String/Boolean [Yes/No]

* denotes compulsory questions.

(Appendix A), the individual fields (corresponding to questions in the survey and metadata) in the dataset are described.

Figure 1 shows the responsive design of our online survey for different screen sizes. A reduced and clear design was utilized with unobtrusive application of key visuals and project color themes for optimum readability and usability.

2.2. Data Format

The data are provided in CSV format with fields separated by semicolon and encoding using UTF-8 charset (see https://doi.org/10.5281/zenodo.5705609 for data download). All survey text is in English language; only free text answers are included in original language as entered by respondents. The first row contains column headers denominating metadata fields and survey questions. Empty field data corresponds to no answer for that particular question. For questions with multiple choice options, each option is included as a separate column with binary status value (“Yes” or “No”). If the option “other” was given, the corresponding field contains the free text value provided by respondents in the original language.

2.3. Survey Respondents

While responses from 32 different countries were registered, participation showed a European focus with respondents from Germany, Austria and Italy contributing 74.2% of all answers (for geographic distribution of respondents, see Figure 2). The majority of participants (36%) worked in public service, followed by the private sector (23%), academia (14%) and NGOs (11%). Respondents’ geographic scope of work was mainly local (35%, city or municipality) or regional (29.5%). Still, 19.4% stated a national and 16.0% an international scope of work.

The mean reported age was 45 years, with 87% of respondents belonging to an age group from 25 to 59 years. Gender distribution was biased towards male respondents who formed the majority of 65%, whereas female respondents contributed 34% of the answers.

2.4. Descriptive Statistics—Key Findings

Survey responses support the general perception that high importance is attributed to data on cycling mobility. Eighty-four percent of respondents rated the importance of cycling data with at least 80 out of 100 points, whereas merely 3% perceived the data as neutral or unimportant (equal to or below 60 points). Regarding current data availability and suitability, 75% of domain professionals stated they were only partly able to solve their tasks using the data available to them, with only 11% being fully able to solve their tasks, and 13% not able to solve them at all from data (see Figure 3).

While 60% of respondents reported they were able to quantify the modal share of cycling for their area of responsibility, only 51% were able to state the main trip purpose based on available data. This number further decreases to only 31% of respondents being able to quantify the average travel distance per cycling trip. Reported numbers show high variation (see Figure 4 for variation in reported trip length as an example). This may partly be explainable by factual variation of the investigated indicators based on location and professional scope as well as by potential uncertainties resulting from data availability and methods applied for deriving trip length. These aspects require further research. Furthermore, different response patterns can be observed depending on participants’ country of origin. However, the sample sizes for single countries are too small for in-depth comparative analysis.

Regarding current data usage, we observed a focus on traditional data sources, whereas high potential was attributed to emerging data sources for the future, as visualized in Figure 5.

Overall, survey responses indicated a gap between the need for cycling data and their current availability and suitability for domain professionals to fulfill their daily tasks in the domain of cycling mobility.

3. Methods

We designed and launched an open snowball survey in six different languages, in order to reach a maximum number of experts for our data acquisition. It consisted of 19 questions divided into four sections. Most questions were set to be obligatory for obtaining comprehensive results. Such obligatory questions were marked with a ‘*’ in the question overview provided in Appendix B. Starting from the German base version, the survey was translated into English, Italian, Dutch, Russian, and Spanish languages by native speakers.

The target group was clearly defined to be domain professionals in planning, politics, the private sector and research within the field of cycling mobility. We invited multipliers from our professional network to distribute the invitation for the online survey among their peers and used national and international mailing lists and social media channels for promoting the survey. There was no technical restriction on survey access, while introductory text and domain-specific questions clearly addressed the aforementioned target group. This approach was chosen in order to offer easy access and to preserve the privacy of participants. However, as a result, the survey data cannot be representative for countries and domains.

For technical implementation of the online survey, we used an instance of the open-source survey software LimeSurvey [10] hosted on infrastructure by the University of Salzburg. It allowed for providing direct web links to specific language versions while retaining the option to manually change the language within the web frontend. For ease of use and recognition value, a simplistic responsive frontend design with minimal project branding was utilized. Screenshots can be found in Appendix A.

Data were exported from our survey webserver in CSV format. Prior to publication, only minor processing was applied to the data: Errors in CSV structure due to CSV separator characters present in free text fields were corrected, and missing century information for participants’ birth years was added. Furthermore, we removed incomplete answers from participants who quit the survey before final submission and excluded internal metadata fields from the dataset. As the field “as_num_trip_length” contained numerous implausible values for average length of bicycle trips, the derived column “as_num_trip_length_corr” was added. It comprises original values converted to integers and values below 50 m multiplied by 1000 in order to represent kilometers. As data showed inconsistencies regarding answers to question 17 (future data sources that currently cannot be accessed) when compared to answers to question 7 (current sources used), we introduced new columns “future_sources_corr_<name>” with answers to question 17 set to “No”, where the corresponding answer to question 7 was “Yes” (except for option “None”).

4. Conclusions

The data from this survey provide valuable insights into how cycling-related data are currently used by professionals. Although an increasing number of data sources have emerged in recent years, and the benefits of data among professionals are evident, we found a gap between current practice and demands. High importance was attributed to cycling data, while only 11% of respondents were able to completely fulfill their daily tasks based on data. Apart from this, we regard this dataset as an important contribution to the discussion on a frequently stated data deluge on the one hand and an experience of data scarcity in many everyday tasks on the other. The findings of this survey study underline the importance of a nuanced approach towards a prevalent data optimism in academia and the private sector. The published dataset serves as a starting point for further investigations of national differences, current practices and demands at various levels of responsibility and of opportunities for translating existing data pools into value creation where data are needed. Future research could benefit from a systematic, representative assessment with balanced contributions at a global scale. Furthermore, assessing the quality of available data appears to be another important research direction.

The lack of suitable and accessible data, which we found as a key result in this study, impedes the implementation of effective and efficient measures for promoting active, sustainable mobility. The results of the survey indicate the benefit of sound data and an evidence basis, respectively, in planning, designing, monitoring and management tasks. Against this backdrop, we call for systematic data generation and provision, as well as for concepts and feasible implementation routines for data fusion and secondary data use.