# Researching vehicles involved in collisions with STATS19 data

    library(stats19)
    library(dplyr)

# Vehicle level variables in the STATS19 datasets

Of the three dataset types in STATS19, the vehicle tables are perhaps the most revealing yet under-explored. They look like this:

    v = get_stats19(year = 2018, type = "vehicles")
    #> Files identified: dftRoadSafetyData_Vehicles_2018.csv
    #>    http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Vehicles_2018.csv
    #> Data already exists in data_dir, not downloading
    #> Data saved at ~/stats19-data/dftRoadSafetyData_Vehicles_2018.csv
    v
    #> # A tibble: 226,409 x 23
    #>    accident_index vehicle_referen… vehicle_type towing_and_arti…
    #>    <chr>                     <int> <chr>        <chr>
    #>  1 2018010080971                 1 Car          No tow/articula…
    #>  2 2018010080971                 2 Taxi/Privat… No tow/articula…
    #>  3 2018010080973                 1 Car          No tow/articula…
    #>  4 2018010080974                 1 Taxi/Privat… No tow/articula…
    #>  5 2018010080974                 2 Car          No tow/articula…
    #>  6 2018010080981                 1 Car          No tow/articula…
    #>  7 2018010080981                 2 Van / Goods… No tow/articula…
    #>  8 2018010080982                 1 Taxi/Privat… No tow/articula…
    #>  9 2018010080982                 2 Car          No tow/articula…
    #> 10 2018010080983                 1 Car          No tow/articula…
    #> # … with 226,399 more rows, and 19 more variables: vehicle_manoeuvre <chr>,
    #> #   vehicle_location_restricted_lane <int>, junction_location <chr>,
    #> #   skidding_and_overturning <chr>, hit_object_in_carriageway <int>,
    #> #   vehicle_leaving_carriageway <int>, hit_object_off_carriageway <int>,
    #> #   first_point_of_impact <chr>, was_vehicle_left_hand_drive <chr>,
    #> #   journey_purpose_of_driver <chr>, sex_of_driver <chr>, age_of_driver <int>,
    #> #   age_band_of_driver <int>, engine_capacity_cc <int>, propulsion_code <chr>,
    #> #   age_of_vehicle <int>, driver_imd_decile <chr>, driver_home_area_type <int>,
    #> #   vehicle_imd_decile <int>

We will categorise the vehicle types to simplify subsequent results:

    v = v %>% mutate(vehicle_type2 = case_when(
      grepl(pattern = "motorcycle", vehicle_type, ignore.case = TRUE) ~ "Motorbike",
      grepl(pattern = "Car", vehicle_type, ignore.case = TRUE) ~ "Car",
      grepl(pattern = "Bus", vehicle_type, ignore.case = TRUE) ~ "Bus",
      grepl(pattern = "cycle", vehicle_type, ignore.case = TRUE) ~ "Cycle",
      # grepl(pattern = "Van", vehicle_type, ignore.case = TRUE) ~ "Van",
      grepl(pattern = "Goods", vehicle_type, ignore.case = TRUE) ~ "Goods",

      TRUE ~ "Other"
    ))
    # barplot(table(v$vehicle_type2))

All of these variables are of potential interest to road safety researchers. Let’s take a look at summaries of a few of them:

    table(v$vehicle_type2)
    #>
    #>       Bus       Car     Cycle     Goods Motorbike     Other
    #>      4918    164645     18125     18020     17890      2811
    summary(v$age_of_driver)
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    #>   -1.00   23.00   35.00   35.52   50.00  101.00
    summary(v$engine_capacity_cc)
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    #>      -1     125    1398    1444    1956   99999
    table(v$propulsion_code)
    #>
    #>         Electric  Electric diesel              Gas      Gas/Bi-fuel
    #>              266               67               31              130
    #>        Heavy oil  Hybrid electric           Petrol Petrol/Gas (LPG)
    #>            77109             3821            93644               29
    summary(v$age_of_vehicle)
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    #>  -1.000  -1.000   5.000   5.727  10.000  84.000

The output shows vehicle type (a wide range of vehicles are represented), age of driver (with young and elderly drivers often seen as more risky), engine capacity and populsion (related to vehicle type and size) and age of vehicle. In addition to these factors appearing in prior road safety research and debate, they are also things that policy makers can influence, e.g by:

- Encouraging modal shift away more dangerous modes and towards safer modes
- Incentivising people in particular risk categories to use safer modes
- Encouraging use of certain (safer) kinds of vehicle, e.g. with tax policies

# Relationships between vehicle type and crash severity

To explore the relationship between vehicles and crash severity, we must first join on the ‘accidents’ table:

    a = get_stats19(year = 2018, type = "accidents")
    #> Files identified: dftRoadSafetyData_Accidents_2018.csv
    #>    http://data.dft.gov.uk.s3.amazonaws.com/road-accidents-safety-data/dftRoadSafetyData_Accidents_2018.csv
    #> Data already exists in data_dir, not downloading
    #> Data saved at ~/stats19-data/dftRoadSafetyData_Accidents_2018.csv
    #> Reading in:
    #> ~/stats19-data/dftRoadSafetyData_Accidents_2018.csv
    #> date and time columns present, creating formatted datetime column
    va = dplyr::inner_join(v, a)
    #> Joining, by = "accident_index"

Now we have additional variables available to us:

    dim(v)
    #> [1] 226409     24
    dim(va)
    #> [1] 226409     56
    names(va)
    #>  [1] "accident_index"
    #>  [2] "vehicle_reference"
    #>  [3] "vehicle_type"
    #>  [4] "towing_and_articulation"
    #>  [5] "vehicle_manoeuvre"
    #>  [6] "vehicle_location_restricted_lane"
    #>  [7] "junction_location"
    #>  [8] "skidding_and_overturning"
    #>  [9] "hit_object_in_carriageway"
    #> [10] "vehicle_leaving_carriageway"
    #> [11] "hit_object_off_carriageway"
    #> [12] "first_point_of_impact"
    #> [13] "was_vehicle_left_hand_drive"
    #> [14] "journey_purpose_of_driver"
    #> [15] "sex_of_driver"
    #> [16] "age_of_driver"
    #> [17] "age_band_of_driver"
    #> [18] "engine_capacity_cc"
    #> [19] "propulsion_code"
    #> [20] "age_of_vehicle"
    #> [21] "driver_imd_decile"
    #> [22] "driver_home_area_type"
    #> [23] "vehicle_imd_decile"
    #> [24] "vehicle_type2"
    #> [25] "location_easting_osgr"
    #> [26] "location_northing_osgr"
    #> [27] "longitude"
    #> [28] "latitude"
    #> [29] "police_force"
    #> [30] "accident_severity"
    #> [31] "number_of_vehicles"
    #> [32] "number_of_casualties"
    #> [33] "date"
    #> [34] "day_of_week"
    #> [35] "time"
    #> [36] "local_authority_district"
    #> [37] "local_authority_highway"
    #> [38] "first_road_class"
    #> [39] "first_road_number"
    #> [40] "road_type"
    #> [41] "speed_limit"
    #> [42] "junction_detail"
    #> [43] "junction_control"
    #> [44] "second_road_class"
    #> [45] "second_road_number"
    #> [46] "pedestrian_crossing_human_control"
    #> [47] "pedestrian_crossing_physical_facilities"
    #> [48] "light_conditions"
    #> [49] "weather_conditions"
    #> [50] "road_surface_conditions"
    #> [51] "special_conditions_at_site"
    #> [52] "carriageway_hazards"
    #> [53] "urban_or_rural_area"
    #> [54] "did_police_officer_attend_scene_of_accident"
    #> [55] "lsoa_of_accident_location"
    #> [56] "datetime"

Let’s see how crash severity relates to the variables of interest mentioned above:

    xtabs(~vehicle_type2 + accident_severity, data = va) %>% prop.table()
    #>              accident_severity
    #> vehicle_type2        Fatal      Serious       Slight
    #>     Bus       0.0002605903 0.0035820131 0.0178791479
    #>     Car       0.0082063876 0.1146465026 0.6043487671
    #>     Cycle     0.0004814296 0.0174993044 0.0620735041
    #>     Goods     0.0020626389 0.0144075545 0.0631202823
    #>     Motorbike 0.0017181296 0.0259574487 0.0513407153
    #>     Other     0.0004019275 0.0025308181 0.0094828386
    xtabs(~vehicle_type2 + accident_severity, data = va) %>% prop.table() %>% plot()

As expected, crashes involving large vehicles such as buses and good vehicles tend to be more serious (involve proportionally more deaths) than crashes involving smaller vehicles.

To focus only on cars, we can filter the `va` table as follows:

    vac = va %>% filter(vehicle_type2 == "Car")

The best proxy we have for car type in the open STATS19 data (there are non-open versions of the data with additional columns) is engine capacity, measured in cubic centimetres (cc). The distribution of engine cc’s in the cars dataset created above is shown below.

    summary(vac$engine_capacity_cc)
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    #>      -1    1198    1560    1440    1968   19997

The output shows that there are some impossible values in the data, likely due to recording error. Very few cars have an engine capacity above 5 litres (5000 cc) and we can be confident that none have an engine capacity below 300 cc. We’ll identify these records and remove them as follows:

We have set the anomolous vehicle size data to NA meaning it will not be used in the subsequent analysis.