driver_fatalities: Traffic Fatalities
In speegled/bayeta:

driver_fatalities

R Documentation

Traffic Fatalities

Description

Number of traffic fatalities in urban areas in January, 2017.

Usage

driver_fatalities

Format

A data frame with 103 observations of 54 variables

cfips: State County FIPS code
statenum: State FIPS
state: State name
stateabbrev: State abbreviation
county: County FIPS
countyname: County name
cbsaname: New MSA Name
year: Year
month: Month
uber_active: Uber active in county
uber_or_lyft: Number of rideshare active
uberx_active: UberX active in county
fatal: Traffic fatalities (M)
drunkdrivera: Fatal accidents in which there was a reported drunk driver
drunkdriverb: Fatalalties in which there was a reported drunk driver
crashes: Crashes with at least one fatality (M)
weekend_holidaya: Fatal accidents on weekend/holidays (M)
weekend_holidayb: Fatalalities on weekend/holidays (M)
fatal2: Log fatalities + 1
fatal3: Fatalities per population
weekend_holidayb2: Log Fatalalities on weekend/holidays + 1
drunkdriverb2: Log Fatalalties in which there was a reported drunk driver + 1
weekend_holidayb3: Fatalalities on weekend/holidays per population
drunkdriverb3: Fatalalties in which there was a reported drunk driver per population
pop: Total county population (Yearly)
vmt_state: State Vehicle Miles Travelled (Monthly)
lengthp2008: County Percent of State Road Length in 2008
vmt: County Vehicle Miles Travelled (Monthly)
unemp: County Unemployment rate (M)
bt: State Beer tax rate (Y)
taxi_county_rate: taxi_msa_rate weighted by countymsa_pop (Y)
decrim: State Has marijuana decriminalization law (M)
gdl: State Has graduated driver-licensing law (M)
hh: State Has hands-free cell phone law (M)
med_mar: State Has medical marijuana legalization law (M)
per_se: State Has drug per se law (M)
primsb: State Has primary seat belt law (M)
secsb: State Has secondary seat belt law (M)
txt_ban: State Has cell phone texting ban law (M)
tpop: Total population year before Uber
lmhh: Log median household income year before Uber
p2039: Percent 20-30 yr old year before Uber
col: Percent college degree year before Uber
vehhh: Percent vehicle ownership year before Uber
alc: Percent excessive drinking year before Uber
vmtt_rate: Public Transportation VMT per Total VMT (Y)
vmtr: Vehicle rev miles travelled - Rail (Y)
vmtr_rate: Rail VMT per Total VMT (Y)
cov_all: Public transit coverage
ser_all: Public transit service frequency
puapop: Percent in census urban area
popdensity: Population density
drinkpld: Number of drinking establishments per area
anyall: Percent any drinking year before Uber

This data is used to investigate the impact of Uber on trafic fatalities. From the authors: "Ridehailing services such as Uber have been promoted as viable interventions for curbing alcohol-involved driving fatalities. However, evidence of ridehailing’s impact has been mixed, with some studies finding no association but others finding either an increase or a decrease in fatalities."