Compute travel times with a detailed breakdown of the routing results#
Detailed itineraries#
In case you are interested in more detailed routing results, you can use
DetailedItinerariesComputer
. In
contrast to TravelTimeMatrixComputer
, it
reports individual trip segments, and possibly multiple alternative routes for
each trip.
As such,
DetailedItinerariesComputer
’s output
is structured in a different way, too. It provides one row per trip segment,
multiple trip segments together constitute a trip option, of which there might
be several per from_id
/to_id
pair. The results also include information on
the public transport routes (e.g., bus line numbers) used on the trip, as well
as a shapely.geometry
for each segment.
Detailed itineraries are computationally expensive
Computing detailed itineraries is significantly more time-consuming than calculating simple travel times. As such, think twice whether you actually need the detailed information output from this function, and how you might be able to limit the number of origins and destinations you need to compute.
For the examples below, to reduce computation effort, we use a sample of 3 origin points and one single destination (the railway station) in our sample data of Helsinki.
import geopandas
import r5py
import r5py.sampledata.helsinki
import shapely
population_grid = geopandas.read_file(r5py.sampledata.helsinki.population_grid)
RAILWAY_STATION = shapely.Point(24.941521, 60.170666)
transport_network = r5py.TransportNetwork(
r5py.sampledata.helsinki.osm_pbf,
[
r5py.sampledata.helsinki.gtfs,
]
)
import datetime
import r5py
origins = population_grid.sample(3).copy()
origins.geometry = origins.geometry.centroid
destinations = geopandas.GeoDataFrame(
{
"id": [1],
"geometry": [RAILWAY_STATION]
},
crs="EPSG:4326",
)
detailed_itineraries_computer = r5py.DetailedItinerariesComputer(
transport_network,
origins=origins,
destinations=destinations,
departure=datetime.datetime(2022,2,22,8,30),
transport_modes=[r5py.TransportMode.TRANSIT, r5py.TransportMode.WALK],
snap_to_network=True,
)
Snap to network
If you read the code block above especially carefully, you may have noticed that
we added an option snap_to_network=True
to
DetailedItinerariesComputer
. This
option does exactly what it says on the outside: it attempts to snap all origin
and destination points to the transport network before routing. This can help
with points that come to lie in an otherwise inaccessible area, such as a fenced
area, a swamp, or the middle of a lake.
For a detailed description of the functionality, see the Advanced use page.
travel_details = detailed_itineraries_computer.compute_travel_details()
travel_details
from_id | to_id | option | segment | transport_mode | departure_time | distance | travel_time | wait_time | route | geometry | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 8 | 1 | 0 | 0 | TransportMode.WALK | NaT | 2462.124000 | 0 days 00:42:06 | NaT | None | LINESTRING (24.91213 60.15755, 24.91224 60.157... |
1 | 8 | 1 | 1 | 0 | TransportMode.WALK | 2022-02-22 08:38:53 | 141.745000 | 0 days 00:02:30 | 0 days 00:00:00 | None | LINESTRING (24.91213 60.15755, 24.91224 60.157... |
2 | 8 | 1 | 1 | 1 | TransportMode.TRAM | 2022-02-22 08:43:00 | 3395.346410 | 0 days 00:08:00 | 0 days 00:01:46 | 9 | LINESTRING (24.91168 60.15681, 24.91187 60.156... |
3 | 8 | 1 | 1 | 2 | TransportMode.WALK | 2022-02-22 08:52:00 | 702.036000 | 0 days 00:11:59 | 0 days 00:00:00 | None | LINESTRING (24.94150 60.17067, 24.94159 60.170... |
4 | 8 | 1 | 2 | 0 | TransportMode.WALK | 2022-02-22 08:35:37 | 340.264000 | 0 days 00:05:49 | 0 days 00:00:00 | None | LINESTRING (24.91213 60.15755, 24.91224 60.157... |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
540 | 23 | 1 | 59 | 1 | TransportMode.TRAM | 2022-02-22 08:48:00 | 734.486981 | 0 days 00:01:00 | 0 days 00:04:38 | 9 | LINESTRING (24.92300 60.16280, 24.92274 60.163... |
541 | 23 | 1 | 59 | 2 | TransportMode.WALK | 2022-02-22 08:50:00 | 1099.593000 | 0 days 00:18:49 | 0 days 00:00:00 | None | LINESTRING (24.94150 60.17067, 24.94159 60.170... |
542 | 23 | 1 | 60 | 0 | TransportMode.WALK | 2022-02-22 08:32:12 | 263.831000 | 0 days 00:04:29 | 0 days 00:00:00 | None | LINESTRING (24.92127 60.16452, 24.92128 60.164... |
543 | 23 | 1 | 60 | 1 | TransportMode.TRAM | 2022-02-22 08:38:00 | 2758.085641 | 0 days 00:06:00 | 0 days 00:01:19 | 9 | LINESTRING (24.92300 60.16280, 24.92274 60.163... |
544 | 23 | 1 | 60 | 2 | TransportMode.WALK | 2022-02-22 08:45:00 | 51.910000 | 0 days 00:00:55 | 0 days 00:00:00 | None | LINESTRING (24.94150 60.17067, 24.94159 60.170... |
545 rows × 11 columns
As you can see, the result contains much more information than earlier. Depending on your screen size, you might even have to scroll further right to see all columns.
Especially in the case of public transport routes, or when choosing a list of
different transport_modes
, also the
table structure of the results is more complex: For each origin/destination
pair, one or more possible option
is reported, which in turn can consist of
one or more segment
s. Both options and segments are numbered sequentially,
starting at 0
.
Each segment, then, represents one row in the results table, and provides information about the transport mode used for a segment, time travelled, possible wait time (before the departure of a public transport vehicle), the route (e.g., bus number, metro line), and finally a line geometry representing the travelled path.
See the following table for a complete list of columns returned by
DetailedItinerariesComputer.compute_travel_details()
:
from_id
(same type asorigins["id"]
)the origin of the trip this segment belongs to
to_id
(same type asdestinations["id"]
)the destination of the trip this segment belongs to
option
(int
)sequential number enumerating the the different trip options found. Each trip option consists of one or more trip segments. (starts with
0
)segment
(int
)sequential number enumerating the segments the current trip option consists of. (starts with
0
)transport_mode
(r5py.TransportMode
)the transport mode used on the current segment
departure_time
(datetime.datetime
)the departure date and time of the public transport vehicle used for the current segment;
NaT
in case of other modes of transportdistance
(float
)the distance travelled on the current segment, in metres. For public transport, see note below.
travel_time
(datetime.timedelta
)The time spent travelling on the current segment
wait_time
(datetime.timedelta
)if the current segment is a public transport vehicle: wait time between the arrival of the previous trip segment and the departure of the current segment.
route
(str
)if the current segment is a public transport vehicle: the route number (or other id), as specified in the input GTFS data set, e.g. bus numbers, metro line names
geometry
(shapely.LineString
)the path travelled on the current segment. For public transport, see note below.
Geometries of public transport routes, and distances travelled
The default upstream version of R⁵ is compiled with
com.conveyal.r5.transit.TransitLayer.SAVE_SHAPES = false
, which improves
performance by not reading the geometries included in GTFS data sets.
As a consequence, the geometry
reported by
DetailedItinerariesComputer
are
straight lines in-between the stops of a public transport line, and do not
reflect the actual path travelled in public transport modes.
With this in mind, r5py does not attempt to compute the distance of public
transport segments if SAVE_SHAPES = false
, as distances would be very crude
approximations, only. Instead it reports NaN
/None
.
The Digital Geography Lab maintains a patched version of R⁵ in its GitHub repositories. If you want to refrain from compiling your own R⁵ jar, but still would like to use detailed geometries of public transport routes, follow the instructions in Advanced use.
Visualise travel details#
It’s not difficult to plot the detailed routes in a map, however, a couple more
steps are needed than with simple travel times.
GeoDataFrame.explore()
cannot handle
the column types r5py.TransportMode
and datetime.timedelta
-
the conversion is quick and easy, though:
travel_details["mode"] = travel_details.transport_mode.astype(str)
travel_details["travel time (min)"] = travel_details.travel_time.apply(
lambda t: round(t.total_seconds() / 60.0, 2)
)
travel_details["trip"] = travel_details.apply(
lambda row: f"{row.from_id} → railway station",
axis=1
)
detailed_routes_map = (
travel_details[
[
"geometry",
"distance",
"mode",
"travel time (min)",
"from_id",
"to_id",
"trip",
"option",
"segment",
]
]
.explore(
tooltip=["trip", "option", "segment", "mode", "travel time (min)", "distance"],
column="mode",
tiles="CartoDB.Positron",
)
)
Let’s also add the origins and the destination to the map:
import folium
import folium.plugins
import pandas
folium.Marker(
(RAILWAY_STATION.y, RAILWAY_STATION.x),
icon=folium.Icon(
color="green",
icon="train",
prefix="fa",
)
).add_to(detailed_routes_map)
points = geopandas.GeoDataFrame(
pandas.DataFrame(
{"id": detailed_itineraries_computer.od_pairs["id_origin"].unique()}
)
.set_index("id")
.join(population_grid.set_index("id"))
.reset_index()
)
points.geometry = points.geometry.to_crs("EPSG:3875").centroid.to_crs("EPSG:4326")
points.apply(
lambda row: (
folium.Marker(
(row["geometry"].y, row["geometry"].x),
icon=folium.plugins.BeautifyIcon(
icon_shape="marker",
number=row["id"],
border_color="#728224",
text_color="#728224",
),
).add_to(detailed_routes_map)
),
axis=1,
)
detailed_routes_map
Export the detailed routes#
If you want to further analyse the resulting routes, for instance, in a desktop
GIS, you can export the
GeoDataFrame
to a wide range of file
formats,
using the to_file()
method.
Note that many geospatial file formats do not support
datetime.timedelta
columns, or columns with custom objects, such as the
r5py.TransportMode
data. Similar to the above example, with a few
simple steps we can convert the values accordingly:
travel_details["transport_mode"] = travel_details.transport_mode.astype(str)
travel_details["travel time (min)"] = travel_details.travel_time.apply(
lambda t: round(t.total_seconds() / 60.0, 2)
)
travel_details["wait time (min)"] = travel_details.wait_time.apply(
lambda t: round(t.total_seconds() / 60.0, 2)
)
# keep all columns except travel time and wait time (which we renamed to
# reflect the unit of measurement)
travel_details = travel_details[
[
"from_id",
"to_id",
"option",
"segment",
"transport_mode",
"departure_time",
"distance",
"travel time (min)",
"wait time (min)",
"route",
"geometry",
]
]
travel_details.to_file("detailed_itineraries.gpkg")