Use information and prediction to reduce disorderly parking

Last registered on July 13, 2021

Pre-Trial

Trial Information

General Information

Title
Use information and prediction to reduce disorderly parking
RCT ID
AEARCTR-0007952
Initial registration date
July 12, 2021
Last updated
July 13, 2021, 9:38 AM EDT

Locations

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Primary Investigator

Affiliation
Beijing Jiaotong University

Other Primary Investigator(s)

PI Affiliation
Department of Economics, School of Economics and Management Beijing Jiaotong University Beijing 100044, China
PI Affiliation
Department of Economics, School of Economics and Management Beijing Jiaotong University Beijing 100044, China
PI Affiliation
Department of Economics, School of Economics and Management Beijing Jiaotong University Beijing 100044, China
PI Affiliation
Graduate School of Economics, University of Tokyo, Tokyo, Japan
PI Affiliation
Department of Economics Business School, Beijing Normal University Beijing 100875, China

Additional Trial Information

Status
On going
Start date
2021-04-28
End date
2021-10-28
Secondary IDs
National Natural Science Foundation of China and the Joint Programming Initiative Urban Europe, National Natural Science Foundation of China
Abstract
Dockless bike-sharing is popular and provides the commuting service for "the last mile of trip". Users often make improper parking and influence the public environment and bicycle circularity. Even some parking locations are already very crowded, users still park shared bicycles here. The company needs to take manpower to carry these bicycles. In our experiment, we use machine learning to predict the destination of users' trips. If users will ride to very busy areas, we send information through app to remind them to avoid parking bicycles here.
External Link(s)

Registration Citation

Citation
He, Haoran et al. 2021. "Use information and prediction to reduce disorderly parking." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.7952-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Reminder: Use app information to remind users to reduce parking in crowded areas.
Social norm information: Use app information to emphasize the proportion of users who parking orderly.
Altruism information: Use app information to emphasize the positive externality that parking orderly.
Social norm and altruism information: Use app information to emphasize the proportion of users who parking orderly and the positive externality that parking orderly.
Intervention Start Date
2021-06-28
Intervention End Date
2021-08-28

Primary Outcomes

Primary Outcomes (end points)
Whether to park bicycles in crowded areas
Primary Outcomes (explanation)
orderly parking

Secondary Outcomes

Secondary Outcomes (end points)
None
Secondary Outcomes (explanation)
None

Experimental Design

Experimental Design
During treatment period, when a subject unlock a shared bicycle mobile, the company will predict the destination of his or her riding. If we forecast that the subject will ride to a very crowded area, the app sends treatment information to them.
Experimental Design Details
Not available
Randomization Method
Completed randomization
Randomization Unit
times
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
One city
Sample size: planned number of observations
Expected over 5000 bike-sharing users
Sample size (or number of clusters) by treatment arms
1000 users for control, 1000 users for reminder group, 1000 users for social norm group, 1000 users for altruism group, 1000 users for Social norm and altruism group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Standard deviation: 20% Percentage: 13% behavioral shift Minimum effect size: 930 of each experimental group, a total of 5580 subjects are needed.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number