Skip to contents

Filter out entire trips which do not meet distance or duration requirements

Source: R/avl_cleaning.R
clean_incomplete_trips.Rd

This function identifies trips that do not meet some acceptable duration and distance traveled ranges, or that have large time or distance gaps in the middle. Violating trips are removed.

Usage

clean_incomplete_trips(
  distance_df,
  max_trip_distance = Inf,
  min_trip_distance = -Inf,
  max_trip_duration = Inf,
  min_trip_duration = -Inf,
  max_distance_gap = Inf,
  max_time_gap = Inf,
  return_removals = FALSE
)

Arguments

distance_df

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

max_trip_distance

Optional. The maximum distance traveled over one trip, in units of input distance. Default is Inf.

min_trip_distance

Optional. The minimum distance traveled over one trip, in units of input distance. Default is -Inf.

max_trip_duration

Optional. The maximum duration of one trip, in seconds. Default is Inf.

min_trip_duration

Optional. The minimum duration of one trip, in seconds. Default is -Inf.

max_distance_gap

Optional. The maximum change in distance between two observations, in units of input distance. Default is Inf.

max_time_gap

Optional. The maximum time between two observations, in seconds. Default is Inf.

return_removals

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

Value

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

Examples

# Set my parameters
my_min_dist <- 1000
my_max_gap <- 1000

# Get input data
lineE_no_jumps <- new_transittraj_data("clean_jumps")
dim(lineE_no_jumps)
#> [1] 3085   10

# Run function
lineE_clean_trips <- clean_incomplete_trips(distance_df = lineE_no_jumps,
                                            min_trip_distance = my_min_dist,
                                            max_distance_gap = my_max_gap)
dim(lineE_clean_trips)
#> [1] 2250   10
head(lineE_clean_trips)
#> # A tibble: 6 × 10
#>   location_ping_id               service_date trip_id_performed speed vehicle_id
#>   <chr>                          <chr>        <chr>             <dbl> <chr>     
#> 1 4af122e0b668d6821335d641a89ad… 2026-05-27   63383915           1.74 1047-1048…
#> 2 ef3b602e52fe3556a7539491e7792… 2026-05-27   63383915           3.31 1047-1048…
#> 3 a940808be7f3a59066c981bffe3e5… 2026-05-27   63383915           2.15 1047-1048…
#> 4 6df05dfca51b44f25d403356de5a3… 2026-05-27   63383915           0    1047-1048…
#> 5 5326947f997dad696a09f510d4857… 2026-05-27   63383915           0    1047-1048…
#> 6 0eeafa189aab82fe0bff169a9dc58… 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>