4. Demonstration on sample data
Data
To illustrate the functionalities of r5r, the package
includes a small sample data for the city of Porto Alegre (Brazil). It
includes seven files:
- An OpenStreetMap network:
poa_osm.pbf - Two public transport feeds:
poa_eptc.zipandpoa_trensurb.zip - A raster elevation data:
poa_elevation.tif - A
poa_hexgrid.csvfile with spatial coordinates of a regular hexagonal grid covering the sample area, which can be used as origin/destination pairs in a travel time matrix calculation. - A
poa_points_of_interest.csvfile containing the names and spatial coordinates of 15 places within Porto Alegre - A
fares_poa.zipfile with the fare rules of the city’s public transport system.
data_path <- system.file("extdata/poa", package = "r5r")
list.files(data_path)
#> [1] "fares" "network.dat"
#> [3] "network_settings.json" "poa_elevation.tif"
#> [5] "poa_eptc.zip" "poa_hexgrid.csv"
#> [7] "poa_osm.pbf" "poa_osm.pbf.mapdb"
#> [9] "poa_osm.pbf.mapdb.p" "poa_points_of_interest.csv"
#> [11] "poa_trensurb.zip"The points of interest data can be seen below. In this example, we will be looking at transport alternatives between some of those places.
poi <- fread(file.path(data_path, "poa_points_of_interest.csv"))
head(poi)
#> id lat lon
#> <char> <num> <num>
#> 1: public_market -30.02756 -51.22781
#> 2: bus_central_station -30.02329 -51.21886
#> 3: gasometer_museum -30.03404 -51.24095
#> 4: santa_casa_hospital -30.03043 -51.22240
#> 5: townhall -30.02800 -51.22865
#> 6: piratini_palace -30.03363 -51.23068The data with origin destination pairs is shown below. In this example, we will be using 200 points randomly selected from this data set.
points <- fread(file.path(data_path, "poa_hexgrid.csv"))
# sample points
sampled_rows <- sample(1:nrow(points), 200, replace=TRUE)
points <- points[ sampled_rows, ]
head(points)
#> id lon lat population schools jobs healthcare
#> <char> <num> <num> <int> <int> <int> <int>
#> 1: 89a9012aec3ffff -51.23281 -30.09186 536 0 191 0
#> 2: 89a9012a20fffff -51.19687 -30.10601 50 0 0 0
#> 3: 89a90129843ffff -51.16308 -30.02291 165 0 1836 0
#> 4: 89a9012801bffff -51.19244 -30.07174 570 0 64 0
#> 5: 89a90128027ffff -51.20423 -30.06241 690 1 43 0
#> 6: 89a90e924c3ffff -51.20806 -30.00659 12 0 978 04.1 Building routable transport network with
setup_r5()
The first step is to build the multimodal transport network used for
routing in R5. This is done with the setup_r5
function. This function does two things: (1) downloads/updates a
compiled JAR file of R5 and stores it locally in the
r5r package directory for future use; and (2) combines the
osm.pbf and gtfs.zip data sets to build a routable network object.
4.2 Accessibility analysis
The fastest way to calculate accessibility estimates is using the
accessibility() function. In this example, we calculate the
number of schools and health care facilities accessible in less than 60
minutes by public transport and walking. More details in this vignette
on Calculating
and visualizing Accessibility.
# set departure datetime input
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
# calculate accessibility
access <- accessibility(r5r_core = r5r_core,
origins = points,
destinations = points,
opportunities_colnames = c("schools", "healthcare"),
mode = c("WALK", "TRANSIT"),
departure_datetime = departure_datetime,
decay_function = "step",
cutoffs = 60
)
head(access)
#> id opportunity percentile cutoff accessibility
#> <char> <char> <int> <int> <num>
#> 1: 89a9012aec3ffff schools 50 60 15
#> 2: 89a9012aec3ffff healthcare 50 60 10
#> 3: 89a9012a20fffff schools 50 60 0
#> 4: 89a9012a20fffff healthcare 50 60 0
#> 5: 89a90129843ffff schools 50 60 19
#> 6: 89a90129843ffff healthcare 50 60 124.3 Routing analysis
For fast routing analysis, r5r currently has three
core functions: travel_time_matrix(),
expanded_travel_time_matrix() and
detailed_itineraries().
Fast many to many travel time matrix
The travel_time_matrix() function is a really simple and
fast function to compute travel time estimates between one or multiple
origin/destination pairs. The origin/destination input can be either a
spatial sf POINT object, or a data.frame
containing the columns id, lon, lat. The function also
receives as inputs the max walking distance, in meters, and the
max trip duration, in minutes. Resulting travel times are also
output in minutes.
This function also allows users to very efficiently capture the travel time uncertainties inside a given time window considering multiple departure times. More info on this vignette.
# set inputs
mode <- c("WALK", "TRANSIT")
max_walk_time <- 30 # minutes
max_trip_duration <- 120 # minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
# calculate a travel time matrix
ttm <- travel_time_matrix(r5r_core = r5r_core,
origins = poi,
destinations = poi,
mode = mode,
departure_datetime = departure_datetime,
max_walk_time = max_walk_time,
max_trip_duration = max_trip_duration)
head(ttm)
#> from_id to_id travel_time_p50
#> <char> <char> <int>
#> 1: public_market public_market 0
#> 2: public_market bus_central_station 14
#> 3: public_market gasometer_museum 12
#> 4: public_market santa_casa_hospital 15
#> 5: public_market townhall 3
#> 6: public_market piratini_palace 17Expanded travel time matrix with minute-by-minute estimates
For those interested in more detailed outputs, the
expanded_travel_time_matrix() works very similarly with
travel_time_matrix() but it brings much more information.
It estimates for each origin destination pair the routes used and total
time disaggregated by access, waiting, in-vehicle and transfer times.
Please note this function can be very memory intensive for large data
sets.
# calculate a travel time matrix
ettm <- expanded_travel_time_matrix(r5r_core = r5r_core,
origins = poi,
destinations = poi,
mode = mode,
departure_datetime = departure_datetime,
breakdown = TRUE,
max_walk_time = max_walk_time,
max_trip_duration = max_trip_duration)
head(ettm)
#> from_id to_id departure_time draw_number access_time wait_time
#> <char> <char> <char> <int> <num> <num>
#> 1: public_market public_market 14:00:00 1 0 0
#> 2: public_market public_market 14:01:00 1 0 0
#> 3: public_market public_market 14:02:00 1 0 0
#> 4: public_market public_market 14:03:00 1 0 0
#> 5: public_market public_market 14:04:00 1 0 0
#> 6: public_market public_market 14:05:00 1 0 0
#> ride_time transfer_time egress_time routes n_rides total_time
#> <num> <num> <num> <char> <int> <num>
#> 1: 0 0 0 [WALK] 0 0
#> 2: 0 0 0 [WALK] 0 0
#> 3: 0 0 0 [WALK] 0 0
#> 4: 0 0 0 [WALK] 0 0
#> 5: 0 0 0 [WALK] 0 0
#> 6: 0 0 0 [WALK] 0 0Detailed itineraries
Most routing packages only return the fastest route. A key advantage
of the detailed_itineraries() function is that is allows
for fast routing analysis while providing multiple alternative routes
between origin destination pairs. The output also brings detailed
information for each route alternative at the trip segment level,
including the transport mode, waiting times, travel time and distance of
each trip segment.
In this example below, we want to know some alternative routes between one origin/destination pair only.
# set inputs
origins <- poi[10,]
destinations <- poi[12,]
mode <- c("WALK", "TRANSIT")
max_walk_time <- 60 # minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
# calculate detailed itineraries
det <- detailed_itineraries(r5r_core = r5r_core,
origins = origins,
destinations = destinations,
mode = mode,
departure_datetime = departure_datetime,
max_walk_time = max_walk_time,
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: -30.05 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 14:09:10 36.2 9460 1 WALK
#> 2 -51.22875 1 14:09:10 36.2 9460 2 RAIL
#> 3 -51.22875 1 14:09:10 36.2 9460 3 WALK
#> 4 -51.22875 1 14:09:10 36.2 9460 4 BUS
#> 5 -51.22875 1 14:09:10 36.2 9460 5 WALK
#> 6 -51.22875 2 14:09:43 48.7 8779 1 WALK
#> segment_duration wait distance route geometry
#> 1 4.6 0.0 174 LINESTRING (-51.1981 -29.99...
#> 2 6.6 1.3 4796 LINHA1 LINESTRING (-51.19763 -29.9...
#> 3 5.7 0.0 256 LINESTRING (-51.22827 -30.0...
#> 4 10.4 4.4 4083 188 LINESTRING (-51.22926 -30.0...
#> 5 3.2 0.0 151 LINESTRING (-51.22949 -30.0...
#> 6 4.6 0.0 174 LINESTRING (-51.1981 -29.99...The output is a data.frame sf object, so we can easily
visualize the results.
Visualize results
Static visualization with ggplot2
package: To provide a geographic context for the visualization of the
results in ggplot2, you can also use the
street_network_to_sf() function to extract the OSM street
network used in the routing.
# extract OSM network
street_net <- street_network_to_sf(r5r_core)
# extract public transport network
transit_net <- r5r::transit_network_to_sf(r5r_core)
# plot
ggplot() +
geom_sf(data = street_net$edges, color='gray85') +
geom_sf(data = det, aes(color=mode)) +
facet_wrap(.~option) +
theme_void()
Cleaning up after usage
r5r objects are still allocated to any amount of memory
previously set after they are done with their calculations. In order to
remove an existing r5r object and reallocate the memory it
had been using, we use the stop_r5 function followed by a
call to Java’s garbage collector, as follows:
If you have any suggestions or want to report an error, please visit the package GitHub page.