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Release: 9/2/2026
Rewritten backend of spatial functions
(get_linear_distances() and
project_onto_route()) to use geos:
For simplicity, all user-facing inputs and outputs still utilize
sf; only backend code has changed.
Performance is improved substantially; up to an
order-of-magnitude processing time improvements for large datasets in
get_linear_distances().
Carto basemaps now require an API key. Removed reliance on or references to Carto in:
Data workflow vignettes mapping raw AVL
(vignette("avl-data-workflow") and
vignette("articles/avl-data-workflow-la")).
Default basemap for plot_animated_map().
Release: 8/11/2026
Publication on CRAN
Completed automated testing suite
Release: 6/7/2026
More substantial improvements to trajectory prediction via
predict(), with some smaller bug fixes related to
visualization.
Trajectory prediction:
Vectorization of deriv input for
new_times and distance_lims/
timestep approach to prediction (#14). Now, a
predict() output with either input type will include a
column deriv, indicating the derivative that row
corresponds to. If a vector of length > 1 is input to
deriv (e.g., deriv = c(0, 1)), then each time
& trip pair will have multiple rows (e.g., a row with
deriv = 0 followed by a row with deriv = 1,
where for each row, interp will correspond to distance or
speed, respectively).
By default, predict() will interpolate at the times
or distances provided for all trips in the trajectory object. Even if an
input dataframe (via new_distances or
new_times) already had point-trip pairs (i.e., had a
trip_id_performed column), each row would be duplicated for
all trips in the trajectory object (#14). This has been changed, so that
if new_distances or new_times already has a
column trip_id_performed, that row will only be
interpolated for that trip.
Visualization:
plot_animated_line() would throw an error when
setting up the x-axis of the ggplot if feature_distances
was not provided (#16). This has been resolved, and a plot can now be
created without features.
plot_interactive_gtfs()’s color_palette
input is now case-insensitive when set to "gtfs"
(#13).
Release: 4/14/2026
Trajectory grouping: new function
group_trajectories() allows the user to group together a
list of single trajectory objects, or split apart a single grouped
trajectory object. Read more at
help(group_trajectories).
predict() for trajectories:
New input parameters for trajectory predict()
methods include distance_lims and timestep.
These allow the user to interpolate at a specific time interval over a
specific region of space. Read more at
vignette("articles/intro-trajectories-la").
Substantial refactoring of internal functions to improve cleanliness and readability.
Release: 3/25/2026
Added examples to all function documentation
GTFS helper functions:
New function get_gtfs_service_dates(): Returns a
dataframe of dates and their service_ids, built from a GTFS
feed’s calendar.txt and/or calendar_dates.txt
file, depending on how the agency has structured these files. Read more
at help(get_gtfs_service_dates).
get_gtfs_trajectory_fun() now works for both methods
of constructing calendar.txt and
calendar_dates.txt (#2), with refactoring through the new
get_gtfs_service_dates(). Read more at
help(get_gtfs_trajectory_fun).
get_stop_distances() will now return all columns in
stops.txt (#4)
Plotting functions now include input parameters
feature_legend, veh_legend, and
traj_legend to override whether a layer’s legend will
appear on the plot. Read more at help(plot_trajectory) and
help(plot_animated_line).
Release: 3/13/2026
Refactoring of plot_trajectory(), and redesign of
how the function chooses points to interpolate over (#3).
If the user specifies a distance_lim, the function
will attempt to use an inverse trajectory function to find appropriate
starting and ending timepoints for interpolation; if an inverse function
does not exist, the user will be informed that interpolation must occur
over the entire trip.
Performance is improved substantially, and performance scales
well with plot_trips length, distance_lim
range, and timestep resolution. Most everyday trajectory
plots generate in less than 1 second, and large plots (with hundreds, or
even thousands, of trajectories) generate in just a few
seconds.
Improved error handling in plot_trajectory(). Error
messages should now better inform the user if filtering (i.e.,
plot_trips and distance_lim) does not contain
any data points.
Refactoring of predict().
New exported function get_trip_extremes() for
extracting the time and distance range of each trip stored in a
trajectory object. Primarily used by predict() and plotting
functions, but may be useful for users. Users can filter the output
dataframe to desired trips. Check out
help(get_trip_extremes) for more information.
Improved performance through restructured table operations when pairing new interpolating points with individual trips (#3).
New dedicated internal validating function for
new_times and new_distances to reduce code
duplication and improve error messages (#2).
Release: 2/27/2026
transittraj for public use.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.