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Incentivizing Commuters to Carpool: A Large Field Experiment with Waze
Last registered on October 08, 2019

Pre-Trial

Trial Information
General Information
Title
Incentivizing Commuters to Carpool: A Large Field Experiment with Waze
RCT ID
AEARCTR-0004822
Initial registration date
October 06, 2019
Last updated
October 08, 2019 1:58 PM EDT
Location(s)
Primary Investigator
Affiliation
McGill University
Other Primary Investigator(s)
PI Affiliation
Google/Waze
PI Affiliation
Google/Waze
PI Affiliation
Google/Waze
Additional Trial Information
Status
Completed
Start date
2019-06-10
End date
2019-07-03
Secondary IDs
Abstract
Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. We find a strong relationship between the affinity to carpool and the potential time saving through an high-occupancy vehicle (HOV) lane. Specifically, we estimate that mentioning the HOV lane increases the click-through rate and conversion rate by 133--185% and 64--141%, respectively relative to sending a generic message.
External Link(s)
Registration Citation
Citation
Cohen, Maxime et al. 2019. "Incentivizing Commuters to Carpool: A Large Field Experiment with Waze." AEA RCT Registry. October 08. https://doi.org/10.1257/rct.4822-1.0.
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Experimental Details
Interventions
Intervention(s)
We sent in-app notifications to Waze users with an offer to try the carpool service. We tested different framings: (A) mentioning the HOV lane and the potentially (high) time saving, (B) mentioning the HOV lane, (C) using a generic carpool offer, and (D) not sending anything.
Intervention Start Date
2019-06-10
Intervention End Date
2019-07-03
Primary Outcomes
Primary Outcomes (end points)
Click-through rate and carpool on-boarding rate for treated and controlled users
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We identify three types of users: (i) users who can save a significant commute time by carpooling through the use of an high-occupancy vehicle (HOV) lane, (ii) users who can still use the HOV lane but with a low time saving, and (iii) users who do not have access to an HOV lane on their commute. For each user, we leverage the Waze data and algorithms to carefully estimate the potential time saving had this user used the HOV lane for his/her commute. Our experimental population comprises 537,370 users across four U.S. states (our analyses are aggregated over hundreds of thousands of users and are meant to be interpreted as statistical averages). We then apply the following set of interventions:

- For commuters with a high time saving---who could save on average 6-40 minutes if they would carpool and use the HOV lane---we randomly split them into four conditions. Each condition involves sending the user an in-app notification with an invitation to try the carpool service. Specifically, we use the following four framings: (A) mentioning the HOV lane and the potentially (high) time saving, (B) mentioning the HOV lane, (C) using a generic carpool offer, and (D) not sending anything.

- For commuters with a low time saving (i.e., users who could save on average 2-5 minutes if they would carpool and use the HOV lane), we randomly split them into the same four conditions.

- For commuters who do not have access to an HOV lane on their commute, we randomly split them into three conditions: (A) mentioning a monetary incentive (receiving $10 as a welcoming bonus to try the carpool service), (B) using a generic carpool offer, and (C) not sending anything. We further carefully select the commuters in this category to be similar to the commuters in the other two categories.

By comparing the different framings used in our experiment, we aim to understand what are the successful triggers that can persuade commuters to carpool. It also allows us to study the tradeoff between saving commute time and earning compensation.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
We have three different groups of users: (i) users who can save a significant commute time by carpooling through the use of an high-occupancy vehicle (HOV) lane, (ii) users who can still use the HOV lane but with a low time saving, and (iii) users who do not have access to an HOV lane on their commute.

For each group, we randomize the users between the different treatments and the control.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Three different groups of users:
H users: users who can save a significant commute time by carpooling through the use of an high-occupancy vehicle (HOV) lane,

L users: users who can still use the HOV lane but with a low time saving, and

N users: users who do not have access to an HOV lane on their commute.

Sample size: planned number of observations
537,370
Sample size (or number of clusters) by treatment arms
84,798 users of type H

224,518 users of type L

228,054 users of type N
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
Documents
Document Name
Academic Paper
Document Type
other
Document Description
File
Academic Paper

MD5: b50c4ed0806b339512e7da683f9636db

SHA1: ecf23921e1e777aed2d97fed8607c2347f6cdd09

Uploaded At: October 06, 2019

IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers