Ohio Rural Rural Multilane Undivided

Locations with Crashes and Operation Speeds

## [1] 552168     48

## rmc_crash1 <- subset(df_RMU, Crash > 0)[!duplicated(subset(df_RMU, Crash > 0)[,4]),]
rmc_crash2= df_RMU[,-c(1)] %>% group_by(TMC) %>% summarize(Crashes=sum(Crash))

datatable(
  rmc_crash2, extensions = 'Buttons', options = list(
    dom = 'Bfrtip',
    buttons = c('csv', 'excel', 'print')
  )
)

Top 10 High Risk Segments