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Remove observations occurring before a trip's minimum distance, or after a trip's maximum distance

Source: R/avl_cleaning.R
trim_trips.Rd

Sometimes AVL pings can be recorded under a trip ID while a vehicle is still traveling in the opposite direction. Conversely, a trip may continue recording as it begins traversing the opposite direction. This function attempts to remove these observations by identifying each trip's minimum (beginning) and maximum (ending) distance, then filtering to only observations between these points. For both ends, the first occurrence of the beginning/ending value is used.

Usage

trim_trips(distance_df, trim_type = "both", return_removals = FALSE)

Arguments

distance_df

A dataframe of linearized AVL data. Must include trip_id_performed, event_timestamp, and distance.

trim_type

Optional. A string, indicating whether the beginning of trips, end of trips, or both beginning and end of trips should be trimmed. Must be one of "beginning", "end", or "both". Default is "both".

return_removals

Optional. A boolean, should the function return a dataframe of points removed and why? Default is FALSE.

Value

The input distance_df with violating points removed. If return_removals = TRUE, a dataframe with observations removed and why.

Examples

# Get input data
lineE_clean_trips <- new_transittraj_data("clean_incomplete_trips")
dim(lineE_clean_trips)
#> [1] 2250   10

# Run function
lineE_trimmed <- trim_trips(distance_df = lineE_clean_trips)
dim(lineE_trimmed)
#> [1] 2130   10
head(lineE_trimmed)
#> # A tibble: 6 × 10
#>   location_ping_id               service_date trip_id_performed speed vehicle_id
#>   <chr>                          <chr>        <chr>             <dbl> <chr>     
#> 1 0eeafa189aab82fe0bff169a9dc58… 2026-05-27   63383915              0 1047-1048…
#> 2 3436b03ae5feecd4947d7a0d1d49d… 2026-05-27   63383915              0 1047-1048…
#> 3 02939a6bc750b44fad6c842fa8be6… 2026-05-27   63383915              0 1047-1048…
#> 4 1a479dd51a750a1e37b7be112ee00… 2026-05-27   63383915              0 1047-1048…
#> 5 55ecfed9996432eeca7dfcb016701… 2026-05-27   63383915              0 1047-1048…
#> 6 822d16b82b32c422ce9d25ec61fe6… 2026-05-27   63383915              0 1047-1048…
#> # ℹ 5 more variables: event_timestamp <dttm>, direction_id <int>,
#> #   shape_id <chr>, route_id <chr>, distance <dbl>