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. Use detailed_itineraries() only if you need suboptimal alternative routes and/or route geometries. For route information by trip segment alone, we strongly recommend expanded_travel_time_matrix().

2. Build routable transport network with build_network()

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

# increase Java memory
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)

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

# 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

• 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.
• 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.

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