Crash Sequence of Events

This vignette explores the vsoe (Vehicle Sequence of Events) data to visualize crash sequence patterns.

vsoe is one of three event-based data files, the others being cevent and vevent. According to the CRSS Analytical User’s Manual, vevent “has the same data elements as the cevent data file” plus “a data element that records the sequential event number for each vehicle,” and the vsoe file “has a subset of the data elements contained in the Vevent data file (it is a simplified vevent data file)” (p. 16). rfars therefore omits cevent and vevent.

First we get one year of data, and filter to the southern region for simplicity.

mydata <- rfars::get_gescrss(years=2021, regions = "s")
#> ✓ 2021 data downloaded
#> Preparing raw data files...
#> ✓ Accident file processed
#> ✓ Vehicle file processed
#> ✓ Person file processed
#> ✓ Weather file(s) processed
#> ✓ Crash risk factors file processed
#> ✓ Vehicle-level files processed
#> ✓ PBtype file processed
#> ✓ SafetyEq file processed
#> ✓ Person-level files processed
#> ✓ Flat file constructed
#> ✓ Multi_acc file constructed
#> ✓ Multi_veh file constructed
#> ✓ Multi_per file constructed
#> ✓ SOE file constructed
#> ✓ Prepared files saved in C:/Users/s87ja/AppData/Local/Temp/RtmpK4ZlC5/GESCRSS data/prepd/2021
#> ✓ Codebook file saved in C:/Users/s87ja/AppData/Local/Temp/RtmpK4ZlC5/GESCRSS data/prepd/

my_events <- mydata$events

The Vsoe data is stored in the events tibble of the object returned by get_gescrss(). Here we see the top 10 individual events:

my_events %>%
  group_by(soe) %>% summarize(n=n()) %>%
  arrange(desc(n)) %>%
  slice(1:10) %>%
  knitr::kable(format = "html")

soe	n
Motor Vehicle In-Transport	47028
Ran Off Roadway - Right	4014
Ran Off Roadway - Left	2977
Parked Motor Vehicle	2044
Rollover/Overturn	1791
Cross Centerline	1475
Tree (Standing Only)	1206
Pedestrian	1202
Ditch	932
Utility Pole/Light Support	770

We can also see the top 10 most common sequences:

my_events %>%
  select(-aoi) %>%
  pivot_wider(names_from = "veventnum", values_from = "soe", values_fill = "x",
              names_prefix = "event") %>%
  select(starts_with("event")) %>%
  group_by_all() %>%
  summarize(n=n(), .groups = "drop") %>%
  arrange(desc(n)) %>%
  slice(1:10) %>%
  select(event1, event2, n) %>%
  knitr::kable(format = "html")

event1	event2	n
Motor Vehicle In-Transport	x	38851
Motor Vehicle In-Transport	Motor Vehicle In-Transport	2248
Parked Motor Vehicle	x	1027
Pedestrian	x	913
Pedalcyclist	x	592
Live Animal	x	543
Cross Centerline	Motor Vehicle In-Transport	392
Ran Off Roadway - Right	Parked Motor Vehicle	269
Motor Vehicle In-Transport	Rollover/Overturn	221
Motor Vehicle In-Transport Strikes or is Struck by Cargo, Persons or Objects Set-in-Motion from/by Another Motor Vehicle In Transport	x	187

Below we consider all state transitions - the transition from one event to the next in the sequence. For example, the sequence A-B-C-D has three transitions: A to B, B to C, and C to D. The graph below shows a subset of the more common transitions in the crash sequences. It is interpreted as follows: the event listed on the x-axis (top) was followed by the event listed on the y-axis. The percentage shown at the graphical intersection represents the percentage of transitions from one event (x) to another event (y). For example, ‘Fence’ was followed by ‘Tree (Standing Only)’ in 21% of sequences. Note that we have added a state labelled ‘Pre-Crash’ to help account for sequences with just one event. Another notable takeaway is the large number of events that precede Rollover/Overturn.

my_events %>%
  group_by(year, casenum, veh_no) %>%
  dplyr::rename(event_to = soe) %>%
  mutate(event_from = data.table::shift(event_to, fill = "Pre-Crash")) %>%
  select(event_from, event_to) %>%
  group_by(event_from, event_to) %>% summarize(n=n()) %>%
  group_by(event_from) %>% mutate(n_from = sum(n)) %>%
  mutate(n_pct = n/n_from) %>%
  filter(n_pct>.2, n>5) %>%
  mutate(
    event_from = ifelse(nchar(event_from)>30, paste0(stringr::str_sub(event_from, 1, 30), "..."), event_from),
    #event_to   = paste0(stringr::str_sub(event_to, 1, 30), "..."),
    event_to = stringr::str_wrap(event_to, 40)
    ) %>%
  filter(event_from != event_to) %>%

  ggplot(aes(x=event_from, y=event_to, fill=n_pct, label=scales::percent(n_pct, accuracy = 1))) +
    viridis::scale_fill_viridis() +
    geom_label() +
    scale_x_discrete(position = "top") +
    theme(
      axis.text.x.top = element_text(angle=45, hjust=0),
      axis.ticks = element_blank(),
      #axis.text.x.bottom = element_text(angle=270, hjust = 0, vjust=.5),
      #legend.position = "none"
      )
#> Adding missing grouping variables: `year`, `casenum`, `veh_no`
#> `summarise()` has grouped output by 'event_from'. You can override using the
#> `.groups` argument.