Impacts of Subsidized Ridesharing on Drunk Driving, Alcohol Consumption, and Mobility
Phase: NA
Conditions: Driving Drunk, Alcohol Drinking
Sponsor: Columbia University

Study Protocol with Statistical Analysis Plan 
Official Title:  Impacts of Subsidized Ridesharing on Drunk Driving, Alcohol Consumption, 
and Mobility 
NCT Number: NCT04949711 
Date:  
January 26, 2026 
 
 

Background 
Alcohol-related road crashes have been shown to be preventable and reductions in traffic crash 
deaths, especially from alcohol, are one of the great public health achievements of the twentieth 
century.1 Between 1982 and 1999, the proportion of fatal crashes involving a driver with a blood 
alcohol concentration 0.08 g/mL fell from 35% to 20%,2 credited largely to key interventions 
introduced during that period (e.g. raising the minimum legal drinking age from 18 to 21 years; 
limiting blood alcohol concentrations for licensed drivers to 0.08 g/mL). After this sustained 
reduction, the incidence of alcohol-related crashes has remained roughly constant for the last two 
decades, and upwards of 12,000 deaths are attributable to alcohol-related crashes each year.3  
 
Achieving further reductions requires concerted efforts to implement evidence-based strategies. 
One intervention that could theoretically reduce alcohol-related motor vehicle crashes is access 
to ridesharing services, such as Uber and Lyft. Ridesharing is a mobile technology that enables 
prospective passengers to summon private owner-operator drivers to specific locations on 
demand. Key advantages of ridesharing over traditional taxi services are their accessibility (Lyft 
and Uber often operate in places taxis do not), ease of payment (users keep their payment source 
on file within the app), transparency (you can track the car’s location and arrival time), and 
safety (your ride is tracked via the app and can be shared with friends or family).  
 
Prospect theory provides a clear mechanism by which this technology will affect alcohol-related 
motor vehicle crashes. This theory of behavioral economics states that individuals make discrete 
choices based on probabilistic assessment of risk.4 A prospective impaired driver will weigh the 
unknown costs of driving after drinking (e.g. crashing, DUI) against the known financial and 
convenience costs associated with using an alternative form of transportation (e.g. public 
transportation, taxis, ridesharing).5-7 Improved access to alternative transportation, including 
ridesharing, will make impaired driving less attractive. Fewer impaired drivers will lead to fewer 
alcohol-related motor vehicle crashes.  
 
The few published studies in this area mostly support the hypothesis that ridesharing will reduce 
alcohol-related motor vehicle crashes. Three studies, including our own contribution, identify 
that locations where ridesharing services are available have fewer alcohol-related crashes and 
fewer DUI arrests.8-10 However, another difference-in-difference analysis conducted in the 100 
most-populous US counties found no association between ridesharing and alcohol-related road 
crash fatalities or all road crash fatalities.11 Despite this sparse and somewhat mixed evidence, 
municipalities have already deployed ridesharing as an intervention to reduce alcohol-related 
motor vehicle crashes. These programs aim to reduce crashes by subsidizing rideshare trips at 
times and places when impaired driving is most common. For example, the Townships of 
Evesham and Voorhees, NJ, provide free trips up to $30 from 21 local bars.12 Our analyses 
identify that this intervention averted 268 (95% CI: 147, 451) alcohol-related crashes and 3 
deaths (95% CI: 2, 5) from 2016 to 2018.  
 
The goal of the current study is to examine ridesharing as a strategy to reduce risks for drunk 
driving among high-risk individuals (e.g. people who go to bars). For this study, we will recruit a 
sample of participants through Facebook advertisements. We will follow the participants for two 
weeks using 3 weekly surveys to assess differential risks for drunk driving for the intervention 
vs. control group. 

Objectives 
The long-term goal of this research is to inform environmental strategies to reduce alcohol-
involved motor vehicle crashes, while minimizing unintended negative consequences, including 
increased alcohol consumption. The broad objective of this intervention study is to test the 
effects of access to subsidized ridesharing on alcohol consumption and risks for drunk driving. 
 
The specific aims of this study are: 
1. Aim 1 (Intended Impacts). To test whether access to subsidized rideshare trips reduces 
individuals’ risks for drunk driving. 
2. Aim 2 (Side Effect). To test whether access to subsidized rideshare trips affects 
individuals’ alcohol consumption. 
3. Aim 3 (Mechanism). To test whether access to ridesharing affects individuals’ mobility, 
including access to alcohol. 
 
 

Methods 
 
Study Design 
This study is a two-arm, parallel-group randomized controlled trial (RCT) to test the effects of 
subsidized ridesharing on self-reported alcohol-impaired driving across 156 US cities. 
Participants are blinded to the overall goal of the study, to the nature of the treatment and control 
conditions, and to their assignment to the treatment and control conditions. This will be 
accomplished through study materials that refer to the study with a generic name (“SafeNights 
Study”) and the nature of the study as examining alcohol consumption and alcohol-related 
harms, without mention of alcohol-impaired driving. 
 
Participants 
Eligible participants: 
1. Reside in a study city; 
2. Are aged 21 years or older; 
3. Have a driver’s license; 
4. Have access to a motor vehicle; 
5. Consumed alcohol at a bar, pub, or nightclub in the past 30 days; 
6. Own a smartphone; 
7. Speak English 
 
Recruitment: 
A two-step approach will be used to recruit the target enrollment of 7,560 participants. First, we 
will randomly select 70 US cities. Eligible cities are census designated places with a population 
over 100,000 and access to Uber ridesharing services. Each eligible city is assigned a randomly 
generated number and then arranged in descending order. Cities are selected for inclusion by 
assessing first- and second-order adjacency. Cities are excluded if they are adjacent to a 
previously included city or its immediate neighbors. This process is repeated until 70 cities are 
selected. In the second step, up to 108 eligible participants are recruited from each city. 
 
After the first year of recruitment, based on enrollment rates, we will estimate progress towards 
the target enrollment. If enrollment rates are too low to reach the target enrollment, we will 
include additional cities. Using the same randomly generated number and adjacency eligibility 
criteria as the original 70 study cities, 86 cities will be added to the sample frame, resulting in 
156 total selected cities. Appendix A contains a list of the randomly selected cities. 
 
While the cities are selected based on shared criteria, we will employ a two-arm, parallel-group 
RCT design where the individual is the unit of randomization. To increase the likelihood of 
balance within cities, we will utilize stratified randomization, using the city of residence as the 
stratification factor. 
 
Enrollment 
Participants will be recruited via social media advertisements placed through the Meta platform 
on Facebook and Instagram. Advertisements will be displayed on Mondays, Tuesdays, and 

Wednesdays. We will display an advertisement to social media users who (i) are aged 21 years or 
older and (ii) “have expressed an interest in or have liked pages related to ‘bars’ AND ‘alcohol.’” 
Eligible individuals who click on the social media advertisement will proceed to the SafeNights 
Study website for screening, informed consent, and an enrollment (Wave 1) survey. 
The survey website contains an initial screening form that confirms participants are eligible to 
participate in the study.  
GPS Sub-Sample. After completing the Wave 1 survey, participants who indicate that they have 
an Android smartphone in the screening form will be invited to join a sub-sample that will 
be followed using GPS tracking data collection. 
 
Informed Consent 
Eligible participants will provide remote e-consent to enroll in the study, using the study website. 
The first page will ask participants to provide remote e-consent to participate in the research. The 
page will be designed so that participants can navigate forwards and backwards, although 
participants who have not e-signed the consent form will not be able to proceed to the enrollment 
survey. If participants do not wish to electronically sign the informed consent form, they will be 
directed to a termination screen that provides information about how to enroll if they change 
their minds. Participants who provide electronically signed informed consent will proceed to the 
enrollment questionnaire. There are several features that we will use in the consenting procedure. 
The first is including the study email address for potential participants who have questions before 
proceeding with the consenting procedure. The system has an embedded digital copy (i.e., a 
PDF) of the consent form in the questionnaire. And, we will add a PDF including the Consent 
Form text, the participant's e-signature, and the signature date that participants will be able to 
download and save. 
 
Enrollment Data Collection 
After providing informed consent, enrolled participants will complete a brief enrollment survey 
asking about their alcohol consumption, risks for drunk driving, and demographic characteristics. 
After completing and submitting the baseline survey, project staff will receive the questionnaire 
data via the secure study website. 
 
Pre- and Post-Intervention Data Collection 
Participants will receive text messages with links to pre-intervention and post-intervention 
surveys on Mondays, Tuesdays and Wednesdays one week (pre-intervention: Days 8 to 10) and 
two weeks (post-intervention: Days 15 to 17) after they complete the enrollment survey. The pre- 
and post-intervention surveys are identical and are designed to be brief versions of the 
enrollment survey. Participants will be asked questions regarding their risks for drunk driving 
over the previous week and alcohol consumption. 
 
Random Allocation 
Randomization will occur at the individual level. To increase the likelihood of balance within 
cities, stratified randomization will allocate participants to the treatment or control conditions 
within cities, allowing for equal numbers of participants to be assigned to the treatment and 
control groups within each selected city. After randomization, participants randomly assigned to 
the treatment condition receive a $35 Uber voucher, and participants randomly assigned to the 
control condition received a $35 Amazon voucher. The compensation for the pre-intervention 

survey serves as the intervention being tested. The randomization procedure is performed using 
computer generated random numbers. 
 
Incentives 
Participants will receive online shopping vouchers for the completion of each survey. 
Participants who complete the enrollment survey receive a $5 Amazon voucher sent via text 
(SMS). Compensation for the pre-intervention survey will follow the procedures described above 
for Random Allocation. For the post-intervention survey, participants not in the GPS sub-sample 
will receive an additional $40 Amazon voucher for completing the survey; participants in the 
GPS sub-sample will receive an additional $80 for completing the post-intervention survey. The 
value of the total available incentives are $80 for the non-GPS sub-sample and $120 for the GPS 
sub-sample. 
 
GPS Sub-Sample 
Immediately after completing the enrollment survey, participants who indicate on the screener 
that they have an Android smartphone will be invited to join a sub-sample that will be followed 
using GPS tracking data collection. We will cap participation in the GPS sub-sample at 2,000 
participants (i.e. 40 from each city). Participants in the GPS sub-sample will be asked to 
download a custom smartphone application through the Google Play app store and will be sent a 
unique passcode by text and email. After downloading the application, participants will enter the 
unique passcode and will complete a further informed consent form that describes the procedures 
for the GPS data collection. GPS tracking will be activated following informed consent 
procedures and will be deactivated immediately after completion of the Wave 3 survey. 
 
 

Statistical Analysis Plan 
We will compare participant characteristics, including demographics and baseline alcohol 
consumption, between the treatment and control groups. All models will be fitted among 
participants who have at least one outcome measurement in the pre- or post-intervention surveys. 
 
Aim 1 
We will conduct an intention-to-treat analysis using generalized estimating equations (GEEs) 
with a logit link to estimate the ratio of odds ratios for alcohol-impaired driving, comparing 
changes from pre- to post-intervention between the intervention and control 
groups. Separate models will be fit for two binary alcohol-impaired driving outcomes: (1) any 
driving after drinking and (2) any driving after drinking “too much.” Each model will include an 
indicator for time (pre- vs. post-intervention), intervention group, and their interaction. To 
address missing data, Multivariate Imputation by Chained Equations (MICE)13 will be used to 
generate multiple imputed datasets. Analyses will be conducted with and without multiple 
imputation, and results will be compared to assess the robustness of the findings. 
 
Aim 2 
We will conduct an intent-to-treat analysis to evaluate the effect of the intervention on changes in 
alcohol consumption. Consumption outcomes will include the frequency (defined as the number 
of drinking days) and continued volume (defined as the number of drinks consumed after 1 drink 
on a drinking day) of drinking per week. We will fit Poisson GEEs to estimate the ratio of rate 
ratio comparing changes in alcohol consumption from pre- to post-intervention between the 
intervention and control groups. Each model will include an indicator for time (pre- vs. post-
intervention), intervention group, and their interaction. We will also examine differences in 
alcohol consumption between licensed venues (bars, pubs, clubs, or restaurants) and other 
locations (e.g., home or friends’ homes). Missing data in alcohol consumption will be addressed 
using MICE. Results from the analyses with and without multiple imputation will be reported. 
 
Aim 3 
Usings records from the GPS sub-sample, we will calculate participants’ exposure to alcohol 
outlets and mobility. We will first clean the GPS data by removing any records that are obtained 
from cell phone towers and that are clearly erroneous (e.g. where individuals travelled > 100 
miles/hour between readings). We will use the GPS data to calculate the following measures: 
1. Time spent near an alcohol outlet is calculated as the sum of the time elapsed between 
GPS records when the participant is within a buffer distance of an alcohol outlet. The 
main measures will use a buffer of 50 meters. Additional measures for use in sensitivity 
analyses will be calculated within buffers of 100 meters and 200 meters. The average 
amount of time spent near an alcohol outlet will provide the average duration of trips to 
an alcohol outlet. 
2. Trips to an alcohol outlet are calculated as the count of times participant is within a buffer 
distance of an alcohol outlet. The main measures will use a buffer of 50 meters. 
Additional measures for use in sensitivity analyses will be calculated within buffers of 
100 meters and 200 meters. The average number of trips to an alcohol outlet will provide 
the average frequency of trips to an alcohol outlet. 
 

References 
1. National Academies of Sciences, Engineering, and Medicine. Getting to Zero Alcohol-
Impaired Driving Fatalities: A Comprehensive Approach to A Persistent Problem. 
Washington, DC: The National Academies Press; 2018 
2. National Highway Traffic Safety Administration. 2016 Motor Vehicle Crashes: Overview. 
Washington, DC: U.S. Department of Transportation; 2017. Retrieved July 23, 2018, from: 
https://crashstats.nhtsa.dot.gov/Api/Public/Publication/812456 
3. National Highway Traffic Safety Administration. 2015 Motor Vehicle Crashes: Overview. 
Washington, DC: U.S. Department of Transportation; 2016. Retrieved February 6, 2018, 
from: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812318 
4. Kahneman D, Tversky A. Choices, values, and frames. American Psychologist 1984; 39: 
341-50. 
5. Homel R. Drivers who drink and rational choice: Random breath testing and the process of 
deterrence. In: Clarke RV, Felson M, eds. Routine Activity and Rational Choice: Advances in 
Criminological Theory. New Brunswick, NJ: Transaction Publishers; 1993. 
6. Clarke RV, Cornish DB. Modeling offenders' decisions: A framework for research and policy. 
Crime and Justice. 1985;6:147-185. 
7. Turrisi R, Jaccard J. Cognitive and attitudinal factors in the analysis of alternatives to drunk 
driving. Journal of Studies on Alcohol. 1992;53(5):405-414. 
8. Brazil N, Kirk DS. Uber and metropolitan traffic fatalities in the United States. American 
Journal of Epidemiology. 2016; 184(3): 192-198. 
9. Dills AK, Mulholland SE. Ride-sharing, fatal crashes, and crime. Southern Economic 
Journal; 2016. Retrieved October 17, 2016, from: https://ssrn.com/abstract=2783797 
10. Greenwood BN, Wattal S. Show me the way to go home: An empirical investigation of 
ridesharing and alcohol related motor vehicle homicide. Fox School of Business Research 
Paper No.15-054; 2015. Retrieved October 17, 2016, from: 
https://ssrn.com/abstract=2557612 
11. Brazil N, Kirk DS. Uber and metropolitan traffic fatalities in the United States. American 
Journal of Epidemiology. 2016; 184(3): 192-198. 
12. Uber. Working Together for Safer Streets in Evesham. San Francisco, CA: Uber 
Technologies. 2018. Retrieved February 6, 2018, from: 
https://www.uber.com/blog/newjersey/working-togetherfor-safer-streets-in-evesham/ 
13. van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations 
in R. Journal of Statistical Software. 2011;45(3):1–67. 
 

Appendix A. List of randomly selected study cities. 
Original Study Cities 
Albuquerque, NM 
Lincoln, NE 
Aurora, CO 
Lowell, MA 
Austin, TX 
Miramar, FL 
Baltimore, MD 
Moreno Valley, CA 
Birmingham, AL 
Nashville, TN 
Burbank, CA 
New Haven, CT 
Charlotte, NC 
New Orleans, LA 
Chattanooga, TN 
New York, NY 
Clearwater, FL 
North Las Vegas, NV 
Cleveland, OH 
Oklahoma City, OK 
Colorado Springs, CO 
Olathe, KS 
Columbus, OH 
Paterson, NJ 
Costa Mesa, CA 
Raleigh, NC 
El Paso, TX 
Rochester, NY 
Escondido, CA 
Roseville, CA 
Everett, WA 
Salinas, CA 
Fairfield, CA 
San Francisco, CA 
Flint, MI 
Santa Clara, CA 
Fontana, CA 
Santa Rosa, CA 
Fort Wayne, IN 
Scottsdale, AZ 
Fort Worth, TX 
Seattle, WA 
Fresno, CA 
Springfield, MA 
Fullerton, CA 
St. Louis, MO 
Grand Rapids, MI 
St. Paul, MN 
Green Bay, WI 
St. Petersburg, FL 
Hartford, CT 
Stamford, CT 
Henderson, NV 
Stockton, CA 
Hialeah, FL 
Syracuse, NY 
Houston, TX 
Tallahassee, FL 
Independence, MO 
Temecula, CA 
Indianapolis, IN 
Vallejo, CA 
Jacksonville, FL 
Vancouver, WA 
Joliet, IL 
Victorville, CA 
Knoxville, TN 
Washington D.C., DC 
Lexington-Fayette, KY 
Westminster, CO 
Additional Study Cities 
Akron, OH 
Little Rock, AR 
Allentown, PA 
Long Beach, CA 
Anchorage, AK 
Louisville, KY 

Ann Arbor, MI 
Lubbock, TX 
Atlanta, GA 
Madison, WI 
Aurora, IL 
Milwaukee, WI 
Bakersfield, CA 
Modesto, CA 
Bellevue, WA 
Montgomery, AL 
Boise City, ID 
Newark, NJ 
Bridgeport, CT 
Newport News, VA 
Buffalo, NY 
Norfolk, VA 
Cambridge, MA 
Norwalk, CA 
Cedar Rapids, IA 
Oceanside, CA 
Chandler, AZ 
Omaha, NE 
Charleston, SC 
Orange, CA 
Chicago, IL 
Orlando, FL 
Chula Vista, CA 
Oxnard, CA 
Cincinnati, OH 
Pasadena, CA 
Columbia, SC 
Philadelphia, PA 
Concord, CA 
Pittsburgh, PA 
Coral Springs, FL 
Plano, TX 
Corona, CA 
Pomona, CA 
Corpus Christi, TX 
Providence, RI 
Des Moines, IA 
Provo, UT 
Detroit, MI 
Reno, NV 
East Los Angeles, CA 
Sacramento, CA 
El Monte, CA 
Salem, OR 
Elgin, IL 
Salt Lake City, UT 
Eugene, OR 
San Antonio, TX 
Fargo, ND 
San Bernardino, CA 
Fort Collins, CO 
Sioux Falls, SD 
Fort Lauderdale, FL 
Spokane, WA 
Fremont, CA 
Spring Valley, NV 
Garland, TX 
Thousand Oaks, CA 
Glendale, AZ 
Topeka, KS 
Gresham, OR 
Torrance, CA 
Honolulu, HI 
Tucson, AZ 
Huntsville, AL 
Tulsa, OK 
Inglewood, CA 
Waterbury, CT 
Jackson, MS 
West Jordan, UT 
Kansas City, KS 
Wichita, KS 
Lakewood, CO 
Wilmington, NC 
Lansing, MI 
Worcester, MA