Estimating Travelers’ Schedule Flexibility: Evidence from Commuters in Germany

Last registered on June 24, 2024


Trial Information

General Information

Estimating Travelers’ Schedule Flexibility: Evidence from Commuters in Germany
Initial registration date
June 04, 2024

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
June 24, 2024, 12:09 PM EDT

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



Primary Investigator

RWI – Leibniz Institute for Economic Research

Other Primary Investigator(s)

PI Affiliation
RWI – Leibniz Institute for Economic Research
PI Affiliation
RWI – Leibniz Institute for Economic Research
PI Affiliation
University of Alabama

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Structural models of congestion explicitly model travelers’ choice of when to travel. These models show that the key parameter determining the social cost of congestion is not the commonly estimated value of time, but rather travelers’ schedule flexibility: a measure of how costly it is for them to arrive earlier or later than desired. Despite the central importance of travelers’ schedule flexibility, there are surprisingly few attempts to estimate it. This is one of the first studies that uses survey data on traveler flexibility. Prior studies have simulated possible gains in terms of time using ZIP codes (workplace and home) as well as departure times, ignoring constraints outside traffic that affect commuting behavior.

We contribute to the existing literature by estimating travelers' schedule flexibility using survey data. We measure commuters' willingness to arrive earlier or later than their current arrival time by quantifying this flexibility in terms of both monetary values and time savings. We use two different elicitation formats - open-ended questions and multiple price lists. In addition, we provide insights into the actual time-saving potential of commuters and the factors that influence their commuting behavior. We ask about commuters' opportunities to work flexibly, their preferred morning start times, their efforts to minimize commuting time, and their reasons for not adjusting their departure times despite potential time savings.
External Link(s)

Registration Citation

Andor, Mark A. et al. 2024. "Estimating Travelers’ Schedule Flexibility: Evidence from Commuters in Germany." AEA RCT Registry. June 24.
Experimental Details


We use two different kinds of elicitation formats (open-ended question and multiple price list) to measure travelers’ schedule flexibility.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Our main outcome is the commuters' willingness to arrive earlier or later than their regular arrival time, which reflects their schedule flexibility.
Primary Outcomes (explanation)
In detail, we elicit both measures of time and money for a certain degree of flexibility and can, by combining those two, assess the monetary value of flexibility.

Secondary Outcomes

Secondary Outcomes (end points)
We further ask for commuters' possibility for working flexible, their preferred morning arriving times at work (or school), and whether they already leave earlier or later to avoid traffic jams, gridlock, delays or overcrowding.
Secondary Outcomes (explanation)
These reasons give us more background information and help us to identify the potential for commuting flexibility, taking into account constraints other than traffic, such as childcare, the possibility to work flexibly (e.g. because of shift work) or the potential to work from home.

Experimental Design

Experimental Design
We randomly assign participants to two groups: for the first group, we elicit commuters' willingness to arrive earlier or later than their regular arrival time using an open-ended question format. The second group is presented with a multiple price list to measure their willingness to arrive earlier than currently; we assess whether they would accept leaving home earlier than their current schedule ffor different monetary values. While our primary goal is to estimate travelers' schedule flexibility, we also want to compare the two elicitation formats. However, this comparison is not the central research question.
Experimental Design Details
Participants are excluded if they are retired or unemployed. Students are included. We further exclude those who cycle or walk to work and those who work entirely from home. Participants are randomly assigned to two groups, excluding anyone with potential morning commute time savings of less than five minutes. Time savings are determined by comparing participants' regular commute durations with their estimated commute times without delays.

Before randomly assigning participants, we assess whether participants' jobs or activities (such as shift work or school) require a fixed start time. This way, we measure the willingness to arrive earlier or later than the desired time for both participants who have flexible schedules (e.g., those with flexitime) and those with fixed schedules (e.g., shift workers, students). So, participants flexible and not flexible are randomly allocated to these two groups. For those who are not flexible, it is a hypothetical setting. Understanding schedule flexibility in this hypothetical context is valuable, as it allows us to assess potential flexibility limited by “external” factors rather than commuters’ habits. These “external” factors could be changed by political action. For instance, schools could consider introducing more flexible schedules, or firms might modify their shift schedules.

Group 1: Participants in this group are asked two questions in an open-ended question format about how much time they would need to save to leave 30 minutes earlier or later for work. They are then asked, in two separate open-ended questions, how much money one would need to give them to leave 30 minutes earlier or later.

Group 2: In the vein of a multiple price list going in certain minute steps participants are asked for each item whether they would leave 30 minutes earlier if they could save the respective amount of time by doing so. This list is limited to the “real” options participants face based on data how long it usually takes them to go to work and how long it would take them in an ideal setting without traffic/delays. In a second step, they receive another list where they can indicate whether they would accept a specific monetary value offered for leaving 30 minutes earlier or not.

Those excluded due to the time saving criterion are individuals who cannot significantly reduce their commute time (less than 5 minutes). All of these participants are given open-ended questions as the first group, to assess their willingness to arrive earlier (not later). This is then a hypothetical setting for the participants.

In addition, we examine commuting preferences, particularly in terms of preferred morning departure times. We also assess factors that influence when participants leave for work (or school), considering reasons such as current traffic conditions, pre-work appointments, dropping off children at school or daycare, and the potential to work from home. We also ask for reasons for not adjusting their departure times despite potential time savings (if there is a potential). These questions are designed to provide a comprehensive understanding of participants' morning commute preferences and the reasons behind them. This helps us to explore the potential for travelers’ schedule flexibility.
Randomization Method
Randomization done in office by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
3,000 individuals
Sample size (or number of clusters) by treatment arms
1,500 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

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Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials