1. Introduction

r5r’s routing and accessibility functions are very fast, but they return only the essential information, and only the optimal route (minimizing travel time and/or monetary cost). Sometimes, though, we would like to do more simple route planning analysis and extract more information for each route. Also, we might be interested in finding not only the fastest route but some other suboptimal route alternatives too. This is where the detailed_itineraries() function comes in. detailed_itineraries() returns, for each origin/destination pair, a detailed route plan per leg, i.e. a part of the trip on a single mode, such as walking to the bus stop (R5’s documentation calls legs ‘segments’). Each leg has its mode, waiting time, travel time, distance and geometry. The function can also return suboptimal route alternatives.

obs. If you only need the routes used and the access, waiting, in-vehicle and transfer times, expanded_travel_time_matrix() is enough. Use detailed_itineraries() when you need each trip segment’s mode, distance or geometry, or suboptimal alternative routes.

2. Build routable transport network with `build_network()`

We use the Porto Alegre (Brazil) sample data included in r5r.

# increase Java memory
old_options <- options(java.parameters = "-Xmx2G")

# load libraries
library(r5r)
library(sf)
library(ggplot2)
library(data.table)

# build a routable transport network with r5r
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

# routing inputs
mode <- c('walk', 'transit')
max_trip_duration <- 60 # minutes

# departure time
departure_datetime <- as.POSIXct("13-05-2019 14:00:00", 
                                 format = "%d-%m-%Y %H:%M:%S")

# load origin/destination points
poi <- fread(file.path(data_path, "poa_points_of_interest.csv"))

3. Detailed info by trip segment for multiple trip alternatives

To get alternative routes between one origin/destination pair, set shortest_path = FALSE. With suboptimal_minutes = 8, r5r also keeps routes arriving up to 8 minutes after the optimal one.

# set inputs
origins <- poi[10,]
destinations <- poi[12,]
mode <- c("WALK", "TRANSIT")
max_walk_time <- 60
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
                                 format = "%d-%m-%Y %H:%M:%S")

# calculate detailed itineraries
det <- detailed_itineraries(
  r5r_network,
  origins = origins,
  destinations = destinations,
  mode = mode,
  departure_datetime = departure_datetime,
  max_walk_time = max_walk_time,
  suboptimal_minutes = 8,
  shortest_path = FALSE
  )

head(det)
#> Simple feature collection with 6 features and 16 fields
#> Geometry type: LINESTRING
#> Dimension:     XY
#> Bounding box:  xmin: -51.24094 ymin: -30.05002 xmax: -51.19762 ymax: -29.99729
#> Geodetic CRS:  WGS 84
#>            from_id  from_lat  from_lon                          to_id    to_lat
#> 1 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995
#> 2 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995
#> 3 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995
#> 4 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995
#> 5 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995
#> 6 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995
#>      to_lon option departure_time total_duration total_distance segment mode
#> 1 -51.22875      1       14:08:16           36.7           9537       1 WALK
#> 2 -51.22875      1       14:08:16           36.7           9537       2 RAIL
#> 3 -51.22875      1       14:08:16           36.7           9537       3 WALK
#> 4 -51.22875      1       14:08:16           36.7           9537       4  BUS
#> 5 -51.22875      1       14:08:16           36.7           9537       5 WALK
#> 6 -51.22875      2       14:08:16           43.8           8850       1 WALK
#>   segment_duration wait distance  route                       geometry
#> 1              5.1  0.0      174        LINESTRING (-51.1981 -29.99...
#> 2              6.6  1.7     4796 LINHA1 LINESTRING (-51.19763 -29.9...
#> 3              4.0  0.0      256        LINESTRING (-51.22827 -30.0...
#> 4             10.4  4.4     4083    188 LINESTRING (-51.22926 -30.0...
#> 5              4.6  0.0      228        LINESTRING (-51.22949 -30.0...
#> 6              5.1  0.0      174        LINESTRING (-51.1981 -29.99...

The output is an sf data.frame, ready to map.

3.1 Visualize results

street_network_to_sf() extracts the OSM street network used in routing, to give the map geographic context:

# extract OSM network
street_net <- r5r::street_network_to_sf(r5r_network)

# extract public transport network
transit_net <- r5r::transit_network_to_sf(r5r_network)

# plot
fig <- ggplot() +
        geom_sf(data = street_net$edges, color='gray85') +
        geom_sf(data = subset(det, option <4), aes(color=mode)) +
        facet_wrap(.~option) + 
        theme_void()

fig

4. Other options

Fastest route only: with the default shortest_path = TRUE and no fare_structure, public transport is routed with R5’s RAPTOR algorithm, the one used by travel_time_matrix(), which is much faster than the search for alternative routes. It routes one departure at each minute of time_window and returns the fastest itinerary.
All origins to all destinations: by default, detailed_itineraries() routes the 1st origin to the 1st destination, the 2nd to the 2nd, and so on. Set all_to_all = TRUE to route every origin to every destination. With shortest_path = TRUE, a single search per origin then serves all its destinations.
No geometry: set drop_geometry = TRUE to return results without the trip geometry. This makes the function faster.

5. Hack for frequency-based GTFS feeds

detailed_itineraries() does not work with frequency-based GTFS feeds. The workaround is to convert frequencies to timetables with the gtfstools package:

library(gtfstools)

# location of your frequency-based GTFS
freq_gtfs_file <- system.file("extdata/spo/spo.zip", package = "r5r")

# read GTFS data
freq_gtfs <- gtfstools::read_gtfs(freq_gtfs_file)

# convert from frequencies to time tables
stop_times_gtfs <- gtfstools::frequencies_to_stop_times(freq_gtfs)

# save it as a new GTFS.zip file
gtfstools::write_gtfs(gtfs = stop_times_gtfs,
                      path = tempfile(pattern = 'stop_times_gtfs', fileext = '.zip'))

write_gtfs() saves the new feed to the path you give it (a temporary file above). Put that file in your data_path, in place of the frequency-based feed, and build the network.

Cleaning up after usage

Stop the network and run Java’s garbage collector to free the memory it used:

r5r::stop_r5(r5r_network)
rJava::.jgc(R.gc = TRUE)
options(old_options)

If you have any suggestions or want to report an error, please visit the package GitHub page.

Trip planning with detailed_itineraries()

2026-11-04

1. Introduction

2. Build routable transport network with `build_network()`

3. Detailed info by trip segment for multiple trip alternatives

3.1 Visualize results

4. Other options

5. Hack for frequency-based GTFS feeds

Cleaning up after usage

Trip planning with detailed_itineraries()

2026-11-04

1. Introduction

2. Build routable transport network with build_network()

3. Detailed info by trip segment for multiple trip alternatives

3.1 Visualize results

4. Other options

5. Hack for frequency-based GTFS feeds

Cleaning up after usage

2. Build routable transport network with `build_network()`