Determinants of Intra-Destination Mobility of Tourists

Last registered on March 23, 2026

Pre-Trial

Trial Information

General Information

Title
Determinants of Intra-Destination Mobility of Tourists
RCT ID
AEARCTR-0018156
Initial registration date
March 17, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 23, 2026, 7:23 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
Technical University of Munich, School of Management

Other Primary Investigator(s)

PI Affiliation
University of Innsbruck
PI Affiliation
University of Innsbruck
PI Affiliation
University of Innsbruck
PI Affiliation
UMIT Tirol

Additional Trial Information

Status
In development
Start date
2026-03-19
End date
2026-03-26
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates factors influencing tourists’ choices between passive and active modes of transportation during short-distance trips at holiday destinations. To examine these preferences, we conduct a single-profile discrete choice experiment (DCE) in which participants repeatedly choose between four transport options: car, e-bike, bicycle, and walking. Each choice situation varies systematically along several attributes, such as distance, weather conditions, infrastructure quality, and other contextual factors.

The primary outcome of interest is whether respondents choose an active mode of transport (walking, bicycle, or e-bike) rather than traveling by car. Using repeated choice data, we estimate the causal effects of the randomized attributes on the probability of choosing active mobility. To further explore heterogeneity in preferences, we conduct subgroup analyses focusing, e.g., on respondents’ physical activity levels, cycling habits, and orientation toward active holidays. These analyses aim to identify which types of tourists are most responsive to conditions that facilitate active mobility. The findings contribute to a better understanding of how destination characteristics and situational factors shape transport choices among tourists and provide insights for designing environments that encourage more active and sustainable mobility behavior during holidays.
External Link(s)

Registration Citation

Citation
Baier, Alexandra et al. 2026. "Determinants of Intra-Destination Mobility of Tourists." AEA RCT Registry. March 23. https://doi.org/10.1257/rct.18156-1.0
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Experimental Details

Interventions

Intervention(s)
This study investigates factors influencing tourists’ choices between passive and active modes of transportation during short-distance trips at holiday destinations. Therefore, we plan to run a large-scale artefactual field experiment with a national representative sample of the German adult population, where respondents take part in a discrete choice experiment (DCE).
Respondents will be asked to choose the mode of transport (walking, bicycle, e-bike, or car) for a hypothetical short-distance trip at the holiday destination. All respondents read a hypothetical scenario in which they are spending a summer holiday in a village in the mountains with another adult. They plan to visit a nearby lake and must choose between different modes of transportation. The scenario differs along several attributes: weather, infrastructure (bike-lane), distance, necessary registration for bike rental, costs for renting an e-bike, flexibility of return of rental bikes, parking costs, availability of parking, and possibility and distance of alternative (scenic) routes.
Respondents are presented with 6 different random scenarios and have to choose the mode of transport.
Intervention Start Date
2026-03-19
Intervention End Date
2026-03-26

Primary Outcomes

Primary Outcomes (end points)
Choice of active mode of transport (walking, bicycle, e-bike) versus car.

We test 9 main hypotheses:
H1: Better weather increases the choice of active modes of transport
H2: Lower distance increases the choice of active modes of transport
H3: Safer bike lanes increase the choice of active modes of transport
H4: Low registration effort increases the choice of active modes of transport
H5: Higher parking costs for cars increase the choice of active modes of transport
H6: A lower number of parking slots increases the choice of active modes of transport
H7: Lower distance for the alternative route increases the choice of active modes of transport
H8: Lower rental cost increases the choice of active modes of transport
H9: Flexible bikes return option increases the choice of active modes of transport

To estimate the average marginal component effect of each attribute on the travel mode, we use the following regression model:

yij = α0 + α1∙weather + α2∙ distance + α3∙bikelane+ α4∙registration+ α5∙cost_parking+ α6∙slots_parking + α7∙detour_distance + α8∙costs_rental + α9∙flex_return + εijk
Primary Outcomes (explanation)
We define a dummy variable that is one if respondents choose an active mode of transport (walking, bicycle, e-bike) and zero if they choose the car.

Secondary Outcomes

Secondary Outcomes (end points)
Choice of bike (e-bike & bike) versus walking.
H2a: Lower distance increases the choice of walking

Choice of e-bike versus bike
H2b: Lower distance increases the choice of taking an e-bike
H7b: Lower distance for alternative route increases the choice of taking an e-bike
H8b: Lower rental cost increases the choice of taking an e-bike

To explore heterogeneity in preferences, we plan to conduct subgroup analyses focusing on respondents’ physical activity levels, cycling habits, and orientation toward active holidays.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We plan to sample a minimum of 2,500 adults aged between 18 and 69 years.

The experiment is conducted in cooperation with a survey institute. The recruitment and polling are managed by the survey institute. We collect the data via an online server (Heroku). Our participants take part in the experiment autonomously on their own digital devices. Randomization is carried out at the individual level, using a computer.

Our experiment is structured as follows:
Respondents will be seeing six consecutive hypothetical scenarios and must choose the mode of transport (walking, bicycle, e-bike, or car).
Experimental Design Details
Not available
Randomization Method
Randomization is carried out by a computer.
Randomization Unit
at the individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2,500
Sample size: planned number of observations
2,500 individuals (adults aged between 18 and 69 years)
Sample size (or number of clusters) by treatment arms
2,500
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
None
IRB

Institutional Review Boards (IRBs)

IRB Name
Leopold-Franzens-Universität Innsbruck, Beirat für ethische Fragen in der wissenschaftlichen Forschung
IRB Approval Date
2026-02-20
IRB Approval Number
17/2025