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USING STATISTICAL APPROACHES TO QUANTIFY THE EFFECTS OF RIDESHARING ACCESSIBILITY ON
DRIVING UNDER THE INFLUENCE (DUI) ARR...

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Presenter Disclosures
(1) The following personal financial relationships with
commercial interests relevant to this presen...

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DUIs and University Culture
• 1,825 college students between
the ages of 18 and 24 die from
alcohol-related unintentional
...

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APHA_presentation_asof102016_edit (1)

  1. 1. USING STATISTICAL APPROACHES TO QUANTIFY THE EFFECTS OF RIDESHARING ACCESSIBILITY ON DRIVING UNDER THE INFLUENCE (DUI) ARRESTS IN A UNIVERSITY CITY Sheldon Waugh, MS and Jacob Ball, MA Thursday, October 20, 2016 Department of Epidemiology College of Public Health and Health Professions University of Florida
  2. 2. Presenter Disclosures (1) The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months: Sheldon Waugh Jacob Ball No relationships to disclose
  3. 3. DUIs and University Culture • 1,825 college students between the ages of 18 and 24 die from alcohol-related unintentional injuries, including motor-vehicle crashes 1. • At age 19, 17% of students drove while intoxicated, 42% drove after drinking any alcohol, and 38% rode with an intoxicated driver 2. 0 10 20 30 40 50 60 18 19 20 21 22 Male Female Percent Binge Alcohol Use in the Past Month among Persons Aged 18 to 22, by College Enrollment Status and Demographic Characteristics (2013) Full Time College Students Other Persons
  4. 4. DUIs and University Culture (College Football) • Student college football fans may represent a significant risk factor for binge drinking, with fans reporting higher alcohol consumption on game days than non fan students3. • Football game days were associated with increased alcohol consumption and higher number of alcohol related arrests2-4.
  5. 5. Ridesharing: a good alternative? • Technologies that utilize smartphones to arrange temporary shared rides in real time. • Popular ridesharing companies use contracted drivers and spatial supply and demand framework to match ride requesters with drivers. • Other positives: • Cheaper than taxis • Higher safety and quality of service • Quicker response times
  6. 6. Uber Safe Rides Program • Originated in 2014 • Enacted to increase use of safe modes of transportation at night • Uber offered cooperative partnerships with large universities • Students with active student ID numbers offered a 50 percent discount for Uber rides within a certain area, including campus • Offered nighttime hours (10pm - 3am) • Paid with university subsidies and student government transportation fee
  7. 7. What’s Missing? - Question - Rationale Little has been done to quantify the effects of these interventions in terms of reducing DUIs arrests. Additionally, significant city events such as college football must be taken in to consideration. • What is the effect of ridesharing on DUI arrests in a university city? • What, if any, effect did the ridesharing subsidy program have on DUI arrests? • To develop a model to appropriately quantify the effects of ridesharing programs combined with mediators and covariates (College Football) known to affect DUI arrests in a university city. • Hypotheses: • Introduction of ridesharing : ↓ DUI Arrests • Introduction of Safe Rides Program : marginal ↓ DUI Arrests
  8. 8. Data • Arrest records and logs were collected from the University police department and city police department • All logged DUI arrests (Driving Under the Influence) were tagged with initial date and time recorded along with unique report ID. DUI data were obtained from January of 2010 to 2016. • Unique SafeRides Program discount requests from students at the university • Unique discount code requests were logged and collected by a Southeastern University participating in the collaborative program, from the beginning of the trial period (April 2015) to December of 2015.
  9. 9. Weekly Time Series (2010-2015) 2010 2011 2012 2013 2014 2015 2016
  10. 10. 0 2 4 6 8 10 12 14 16 18 20 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129 133 137 141 145 149 153 157 161 165 169 173 177 181 185 189 193 197 201 205 209 213 217 221 225 229 233 237 241 245 249 253 257 261 265 269 273 277 281 285 289 293 297 301 305 309 313 Weekly DUIs UBER Introduced UBER SR Program Introduced
  11. 11. 0 500 1000 1500 2000 2500 3000 Requests Safe Rides Program Discount Code Requests Student Newspaper Facebook (via Student Govt.) Instagram (via Student Govt.) Twitter (via Student Govt.)
  12. 12. 0 2 3 1 4
  13. 13. Methods • A 5 year (2010-2015) weekly Poisson regression model • Response variable: Weekly Counts of DUI Arrests • Variables: Year, Presence of Football, Interaction term (Football and Year), School Semester, Quintile cumulative discount downloads, Presence of Uber (July 2014) • A 2 year daily Hurdle regression model (2014-2015) • Hurdle regression analysis which employs a logistic regression for the presence/absence of weeks with at least one DUI, and then a truncated Poisson or negative binomial regression to predict the magnitude of DUIs given there is at least one in a given week. • Response variable: Daily Counts of DUI Arrests • Variables: Weekday, Year, Month, College football game site, Quintile cumulative discount downloads, Presence of Uber (July 2014) Arrests only from Wednesday to Saturday are included in the model. • OR/IRR > 1: Adverse effect • OR/IRR < 1: Protective effect
  14. 14. Year Year and Football Safe Rides Program Semester Weekly Poisson Regression
  15. 15. Uber Safe Rides Football Game Site Year Controlling for: Weekday, Year, Month, Daily Hurdle Regression
  16. 16. Conclusions • Marginal effect of ridesharing • Introduction of Safe Rides Program : marginal ↓ DUI Arrests • Additional variables are necessary to improve the quantification of ridesharing demand and use. • Surge (Uber) • Police presence may be another variable to observe in future studies.
  17. 17. References 1. Hingson, R. W., Zha, W. & Weitzman, E. R. Magnitude of and Trends in Alcohol-Related Mortality and Morbidity Among U.S. College Students Ages 18-24, 1998-2005. J Stud Alcohol Drugs Suppl 12–20 (2009). 2. Beck, K. H. et al. Trends in alcohol-related traffic risk behaviors among college students. Alcohol. Clin. Exp. Res. 34, 1472–1478 (2010). 3. Tavis Glassman MPH, Mse., PhD;, C. E. W., Edessa Jobli MD, M. & PhD, H. B. Alcohol- Related Fan Behavior on College Football Game Day. Journal of American College Health 56, 255–260 (2007). 4. Merlo, L. J., Hong, J. & Cottler, L. B. The association between alcohol-related arrests and college football game days. Drug and Alcohol Dependence 106, 69–71 (2010). 5. Merlo, L. J., Ahmedani, B. K., Barondess, D. A., Bohnert, K. M. & Gold, M. S. Alcohol consumption associated with collegiate American football pre-game festivities. Drug and Alcohol Dependence 116, 242–245 (2011). 6. Rees, D. I. & Schnepel, K. T. College Football Games and Crime. Journal of Sports Economics 10, 68–87 (2009).

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