Sample Data¶
The documentation provides very simple example queries based on a small sample network that resembles a city. To be able to execute the mayority of the examples queries, follow the instructions bellow.
Main graph¶
A graph consists of a set of edges and a set of vertices.
The following city is to be inserted into the database:
Information known at this point is the geometry of the edges, cost values, cpacity values, category values and some locations that are not in the graph.
The process to have working topology starts by inserting the edges. After that everything else is calculated.
Edges¶
The database design for the documentation of pgRouting, keeps in the same row 2
segments, one in the direction of the geometry and the second in the oposite
direction. Therfore some information has the reverse_ prefix which
corresponds to the segment on the oposite direction of the geometry.
Column |
Description |
|---|---|
|
A unique identifier. |
|
Identifier of the starting vertex of the geometry |
|
Identifier of the ending vertex of the geometry |
|
Cost to traverse from source to |
|
Cost to traverse from target to |
|
Flow capacity from source to |
|
Flow capacity from target to |
|
Flow capacity from target to |
|
Flow capacity from target to |
|
\(x\) coordinate of the starting vertex of the geometry.
|
|
\(y\) coordinate of the ending vertex of the geometry.
|
|
The geometry of the segments. |
CREATE TABLE edges (
id BIGSERIAL PRIMARY KEY,
source BIGINT,
target BIGINT,
cost FLOAT,
reverse_cost FLOAT,
capacity BIGINT,
reverse_capacity BIGINT,
x1 FLOAT,
y1 FLOAT,
x2 FLOAT,
y2 FLOAT,
geom geometry
);
CREATE TABLE
Starting on PostgreSQL 12:
...
x1 FLOAT GENERATED ALWAYS AS (ST_X(ST_StartPoint(geom))) STORED,
y1 FLOAT GENERATED ALWAYS AS (ST_Y(ST_StartPoint(geom))) STORED,
x1 FLOAT GENERATED ALWAYS AS (ST_X(ST_EndPoint(geom))) STORED,
y1 FLOAT GENERATED ALWAYS AS (ST_Y(ST_EndPoint(geom))) STORED,
...
Optionally indexes on different columns can be created. The recomendation is to have
idindexed.sourceandtargetcolumns indexed to speed up pgRouting queries.geomindexed to speed up gemetry processes that might be needed in the front end.
For this small example the indexes are skipped, except for id
Edges data¶
Inserting into the database the information of the edges:
INSERT INTO edges (
cost, reverse_cost,
capacity, reverse_capacity, geom) VALUES
( 1, 1, 80, 130, ST_MakeLine(ST_POINT(2, 0), ST_POINT(2, 1))),
(-1, 1, -1, 100, ST_MakeLine(ST_POINT(2, 1), ST_POINT(3, 1))),
(-1, 1, -1, 130, ST_MakeLine(ST_POINT(3, 1), ST_POINT(4, 1))),
( 1, 1, 100, 50, ST_MakeLine(ST_POINT(2, 1), ST_POINT(2, 2))),
( 1, -1, 130, -1, ST_MakeLine(ST_POINT(3, 1), ST_POINT(3, 2))),
( 1, 1, 50, 100, ST_MakeLine(ST_POINT(0, 2), ST_POINT(1, 2))),
( 1, 1, 50, 130, ST_MakeLine(ST_POINT(1, 2), ST_POINT(2, 2))),
( 1, 1, 100, 130, ST_MakeLine(ST_POINT(2, 2), ST_POINT(3, 2))),
( 1, 1, 130, 80, ST_MakeLine(ST_POINT(3, 2), ST_POINT(4, 2))),
( 1, 1, 130, 50, ST_MakeLine(ST_POINT(2, 2), ST_POINT(2, 3))),
( 1, -1, 130, -1, ST_MakeLine(ST_POINT(3, 2), ST_POINT(3, 3))),
( 1, -1, 100, -1, ST_MakeLine(ST_POINT(2, 3), ST_POINT(3, 3))),
( 1, -1, 100, -1, ST_MakeLine(ST_POINT(3, 3), ST_POINT(4, 3))),
( 1, 1, 80, 130, ST_MakeLine(ST_POINT(2, 3), ST_POINT(2, 4))),
( 1, 1, 80, 50, ST_MakeLine(ST_POINT(4, 2), ST_POINT(4, 3))),
( 1, 1, 80, 80, ST_MakeLine(ST_POINT(4, 1), ST_POINT(4, 2))),
( 1, 1, 130, 100, ST_MakeLine(ST_POINT(0.5, 3.5), ST_POINT(1.999999999999, 3.5))),
( 1, 1, 50, 130, ST_MakeLine(ST_POINT(3.5, 2.3), ST_POINT(3.5, 4)));
INSERT 0 18
Negative values on the cost, capacity and category means that the edge do not exist.
Vertices¶
The vertex information is calculated based on the identifier of the edge and the geometry and saved on a table. Saving all the information provided by pgr_extractVertices – Proposed:
SELECT * INTO vertices
FROM pgr_extractVertices('SELECT id, geom FROM edges ORDER BY id');
SELECT 17
In this case the because the CREATE statement was not used, the definition
of an index on the table is needed.
CREATE SEQUENCE vertices_id_seq;
CREATE SEQUENCE
ALTER TABLE vertices ALTER COLUMN id SET DEFAULT nextval('vertices_id_seq');
ALTER TABLE
ALTER SEQUENCE vertices_id_seq OWNED BY vertices.id;
ALTER SEQUENCE
SELECT setval('vertices_id_seq', (SELECT coalesce(max(id)) FROM vertices));
setval
--------
17
(1 row)
The structure of the table is:
Table "public.vertices"
Column | Type | Collation | Nullable | Default
-----------+------------------+-----------+----------+--------------------------------------
id | bigint | | | nextval('vertices_id_seq'::regclass)
in_edges | bigint[] | | |
out_edges | bigint[] | | |
x | double precision | | |
y | double precision | | |
geom | geometry | | |
Vertices data¶
The saved information of the vertices is:
SELECT * FROM vertices;
id | in_edges | out_edges | x | y | geom
----+----------+-----------+----------------+-----+--------------------------------------------
1 | | {6} | 0 | 2 | 010100000000000000000000000000000000000040
2 | | {17} | 0.5 | 3.5 | 0101000000000000000000E03F0000000000000C40
3 | {6} | {7} | 1 | 2 | 0101000000000000000000F03F0000000000000040
4 | {17} | | 1.999999999999 | 3.5 | 010100000068EEFFFFFFFFFF3F0000000000000C40
5 | | {1} | 2 | 0 | 010100000000000000000000400000000000000000
6 | {1} | {2,4} | 2 | 1 | 01010000000000000000000040000000000000F03F
7 | {4,7} | {8,10} | 2 | 2 | 010100000000000000000000400000000000000040
8 | {10} | {12,14} | 2 | 3 | 010100000000000000000000400000000000000840
9 | {14} | | 2 | 4 | 010100000000000000000000400000000000001040
10 | {2} | {3,5} | 3 | 1 | 01010000000000000000000840000000000000F03F
11 | {5,8} | {9,11} | 3 | 2 | 010100000000000000000008400000000000000040
12 | {11,12} | {13} | 3 | 3 | 010100000000000000000008400000000000000840
13 | | {18} | 3.5 | 2.3 | 01010000000000000000000C406666666666660240
14 | {18} | | 3.5 | 4 | 01010000000000000000000C400000000000001040
15 | {3} | {16} | 4 | 1 | 01010000000000000000001040000000000000F03F
16 | {9,16} | {15} | 4 | 2 | 010100000000000000000010400000000000000040
17 | {13,15} | | 4 | 3 | 010100000000000000000010400000000000000840
(17 rows)
Here is where adding more columns to the vertices table can be done. Additional columns names and types will depend on the application.
The topology¶
This queries based on the vertices data create a topology by filling the
source and target columns in the edges table.
/* -- set the source information */
UPDATE edges AS e
SET source = v.id, x1 = x, y1 = y
FROM vertices AS v
WHERE ST_StartPoint(e.geom) = v.geom;
UPDATE 18
/* -- set the target information */
UPDATE edges AS e
SET target = v.id, x2 = x, y2 = y
FROM vertices AS v
WHERE ST_EndPoint(e.geom) = v.geom;
UPDATE 18
Topology data¶
SELECT id, source, target
FROM edges ORDER BY id;
id | source | target
----+--------+--------
1 | 5 | 6
2 | 6 | 10
3 | 10 | 15
4 | 6 | 7
5 | 10 | 11
6 | 1 | 3
7 | 3 | 7
8 | 7 | 11
9 | 11 | 16
10 | 7 | 8
11 | 11 | 12
12 | 8 | 12
13 | 12 | 17
14 | 8 | 9
15 | 16 | 17
16 | 15 | 16
17 | 2 | 4
18 | 13 | 14
(18 rows)
Points outside the graph¶
Points of interest¶
Some times the applications work “on the fly” starting from a location that is not a vertex in the graph. Those locations, in pgRrouting are called points of interest.
The information needed in the points of interest is pid, edge_id,
side, fraction.
On this documentation there will be some 6 fixed points of interest and they will be stored on a table.
Column |
Description |
|---|---|
|
A unique identifier. |
|
Identifier of the edge nearest edge that allows an arrival to the point. |
|
Is it on the left, right or both sides of the segment |
|
Where in the segment is the point located. |
|
The geometry of the points. |
|
The geometry of the points moved on top of the segment. |
CREATE TABLE pointsOfInterest(
pid BIGSERIAL PRIMARY KEY,
edge_id BIGINT,
side CHAR,
fraction FLOAT,
geom geometry);
CREATE TABLE
Points of interest fillup¶
INSERT INTO pointsOfInterest (edge_id, side, fraction, geom) VALUES
(1, 'l' , 0.4, ST_POINT(1.8, 0.4)),
(15, 'r' , 0.4, ST_POINT(4.2, 2.4)),
(12, 'l' , 0.6, ST_POINT(2.6, 3.2)),
(6, 'r' , 0.3, ST_POINT(0.3, 1.8)),
(5, 'l' , 0.8, ST_POINT(2.9, 1.8)),
(4, 'b' , 0.7, ST_POINT(2.2, 1.7));
INSERT 0 6
Support tables¶
Combinations¶
Many functions can be used with a combinations of (source, target) pairs
when wanting a route from source to target.
For convinence of this documentations, some combinations will be stored on a table:
CREATE TABLE combinations (
source BIGINT,
target BIGINT
);
CREATE TABLE
Inserting the data:
INSERT INTO combinations (
source, target) VALUES
(5, 6),
(5, 10),
(6, 5),
(6, 15),
(6, 14);
INSERT 0 5
Combinations data¶
SELECT * FROM combinations;
source | target
--------+--------
5 | 6
5 | 10
6 | 5
6 | 15
6 | 14
(5 rows)
Restrictions¶
Some functions accept soft restrictions about the segments.
The creation of the restrictions table
CREATE TABLE restrictions (
id SERIAL PRIMARY KEY,
path BIGINT[],
cost FLOAT
);
CREATE TABLE
Adding the restrictions
INSERT INTO restrictions (path, cost) VALUES
(ARRAY[4, 7], 100),
(ARRAY[8, 11], 100),
(ARRAY[7, 10], 100),
(ARRAY[3, 5, 9], 4),
(ARRAY[9, 16], 100);
INSERT 0 5
Restrictions data¶
SELECT * FROM restrictions;
id | path | cost
----+---------+------
1 | {4,7} | 100
2 | {8,11} | 100
3 | {7,10} | 100
4 | {3,5,9} | 4
5 | {9,16} | 100
(5 rows)
Images¶
Red arrows correspond when
cost> 0 in the edge table.Blue arrows correspond when
reverse_cost> 0 in the edge table.Points are outside the graph.
Click on the graph to enlarge.
Directed graph with cost and reverse_cost¶
When working with city networks, this is recommended for point of view of vehicles.
Directed, with cost and reverse_cost¶
Undirected graph with cost and reverse_cost¶
When working with city networks, this is recommended for point of view of pedestrians.
Undirected, with cost and reverse cost¶
Directed graph with cost¶
Directed, with cost¶
Undirected graph with cost¶
Undirected, with cost¶
Pick & Deliver Data¶
This data example lc101 is from data published at https://www.sintef.no/projectweb/top/pdptw/li-lim-benchmark/
The vehicles¶
There are 25 vehciles in the problem all with the same characteristics.
CREATE TABLE v_lc101(
id BIGINT NOT NULL primary key,
capacity BIGINT DEFAULT 200,
start_x FLOAT DEFAULT 30,
start_y FLOAT DEFAULT 50,
start_open INTEGER DEFAULT 0,
start_close INTEGER DEFAULT 1236);
CREATE TABLE
/* create 25 vehciles */
INSERT INTO v_lc101 (id)
(SELECT * FROM generate_series(1, 25));
INSERT 0 25
The original orders¶
The data comes in different rows for the pickup and the delivery of the same order.
CREATE table lc101_c(
id BIGINT not null primary key,
x DOUBLE PRECISION,
y DOUBLE PRECISION,
demand INTEGER,
open INTEGER,
close INTEGER,
service INTEGER,
pindex BIGINT,
dindex BIGINT
);
CREATE TABLE
/* the original data */
INSERT INTO lc101_c(
id, x, y, demand, open, close, service, pindex, dindex) VALUES
( 1, 45, 68, -10, 912, 967, 90, 11, 0),
( 2, 45, 70, -20, 825, 870, 90, 6, 0),
( 3, 42, 66, 10, 65, 146, 90, 0, 75),
( 4, 42, 68, -10, 727, 782, 90, 9, 0),
( 5, 42, 65, 10, 15, 67, 90, 0, 7),
( 6, 40, 69, 20, 621, 702, 90, 0, 2),
( 7, 40, 66, -10, 170, 225, 90, 5, 0),
( 8, 38, 68, 20, 255, 324, 90, 0, 10),
( 9, 38, 70, 10, 534, 605, 90, 0, 4),
( 10, 35, 66, -20, 357, 410, 90, 8, 0),
( 11, 35, 69, 10, 448, 505, 90, 0, 1),
( 12, 25, 85, -20, 652, 721, 90, 18, 0),
( 13, 22, 75, 30, 30, 92, 90, 0, 17),
( 14, 22, 85, -40, 567, 620, 90, 16, 0),
( 15, 20, 80, -10, 384, 429, 90, 19, 0),
( 16, 20, 85, 40, 475, 528, 90, 0, 14),
( 17, 18, 75, -30, 99, 148, 90, 13, 0),
( 18, 15, 75, 20, 179, 254, 90, 0, 12),
( 19, 15, 80, 10, 278, 345, 90, 0, 15),
( 20, 30, 50, 10, 10, 73, 90, 0, 24),
( 21, 30, 52, -10, 914, 965, 90, 30, 0),
( 22, 28, 52, -20, 812, 883, 90, 28, 0),
( 23, 28, 55, 10, 732, 777, 0, 0, 103),
( 24, 25, 50, -10, 65, 144, 90, 20, 0),
( 25, 25, 52, 40, 169, 224, 90, 0, 27),
( 26, 25, 55, -10, 622, 701, 90, 29, 0),
( 27, 23, 52, -40, 261, 316, 90, 25, 0),
( 28, 23, 55, 20, 546, 593, 90, 0, 22),
( 29, 20, 50, 10, 358, 405, 90, 0, 26),
( 30, 20, 55, 10, 449, 504, 90, 0, 21),
( 31, 10, 35, -30, 200, 237, 90, 32, 0),
( 32, 10, 40, 30, 31, 100, 90, 0, 31),
( 33, 8, 40, 40, 87, 158, 90, 0, 37),
( 34, 8, 45, -30, 751, 816, 90, 38, 0),
( 35, 5, 35, 10, 283, 344, 90, 0, 39),
( 36, 5, 45, 10, 665, 716, 0, 0, 105),
( 37, 2, 40, -40, 383, 434, 90, 33, 0),
( 38, 0, 40, 30, 479, 522, 90, 0, 34),
( 39, 0, 45, -10, 567, 624, 90, 35, 0),
( 40, 35, 30, -20, 264, 321, 90, 42, 0),
( 41, 35, 32, -10, 166, 235, 90, 43, 0),
( 42, 33, 32, 20, 68, 149, 90, 0, 40),
( 43, 33, 35, 10, 16, 80, 90, 0, 41),
( 44, 32, 30, 10, 359, 412, 90, 0, 46),
( 45, 30, 30, 10, 541, 600, 90, 0, 48),
( 46, 30, 32, -10, 448, 509, 90, 44, 0),
( 47, 30, 35, -10, 1054, 1127, 90, 49, 0),
( 48, 28, 30, -10, 632, 693, 90, 45, 0),
( 49, 28, 35, 10, 1001, 1066, 90, 0, 47),
( 50, 26, 32, 10, 815, 880, 90, 0, 52),
( 51, 25, 30, 10, 725, 786, 0, 0, 101),
( 52, 25, 35, -10, 912, 969, 90, 50, 0),
( 53, 44, 5, 20, 286, 347, 90, 0, 58),
( 54, 42, 10, 40, 186, 257, 90, 0, 60),
( 55, 42, 15, -40, 95, 158, 90, 57, 0),
( 56, 40, 5, 30, 385, 436, 90, 0, 59),
( 57, 40, 15, 40, 35, 87, 90, 0, 55),
( 58, 38, 5, -20, 471, 534, 90, 53, 0),
( 59, 38, 15, -30, 651, 740, 90, 56, 0),
( 60, 35, 5, -40, 562, 629, 90, 54, 0),
( 61, 50, 30, -10, 531, 610, 90, 67, 0),
( 62, 50, 35, 20, 262, 317, 90, 0, 68),
( 63, 50, 40, 50, 171, 218, 90, 0, 74),
( 64, 48, 30, 10, 632, 693, 0, 0, 102),
( 65, 48, 40, 10, 76, 129, 90, 0, 72),
( 66, 47, 35, 10, 826, 875, 90, 0, 69),
( 67, 47, 40, 10, 12, 77, 90, 0, 61),
( 68, 45, 30, -20, 734, 777, 90, 62, 0),
( 69, 45, 35, -10, 916, 969, 90, 66, 0),
( 70, 95, 30, -30, 387, 456, 90, 81, 0),
( 71, 95, 35, 20, 293, 360, 90, 0, 77),
( 72, 53, 30, -10, 450, 505, 90, 65, 0),
( 73, 92, 30, -10, 478, 551, 90, 76, 0),
( 74, 53, 35, -50, 353, 412, 90, 63, 0),
( 75, 45, 65, -10, 997, 1068, 90, 3, 0),
( 76, 90, 35, 10, 203, 260, 90, 0, 73),
( 77, 88, 30, -20, 574, 643, 90, 71, 0),
( 78, 88, 35, 20, 109, 170, 0, 0, 104),
( 79, 87, 30, 10, 668, 731, 90, 0, 80),
( 80, 85, 25, -10, 769, 820, 90, 79, 0),
( 81, 85, 35, 30, 47, 124, 90, 0, 70),
( 82, 75, 55, 20, 369, 420, 90, 0, 85),
( 83, 72, 55, -20, 265, 338, 90, 87, 0),
( 84, 70, 58, 20, 458, 523, 90, 0, 89),
( 85, 68, 60, -20, 555, 612, 90, 82, 0),
( 86, 66, 55, 10, 173, 238, 90, 0, 91),
( 87, 65, 55, 20, 85, 144, 90, 0, 83),
( 88, 65, 60, -10, 645, 708, 90, 90, 0),
( 89, 63, 58, -20, 737, 802, 90, 84, 0),
( 90, 60, 55, 10, 20, 84, 90, 0, 88),
( 91, 60, 60, -10, 836, 889, 90, 86, 0),
( 92, 67, 85, 20, 368, 441, 90, 0, 93),
( 93, 65, 85, -20, 475, 518, 90, 92, 0),
( 94, 65, 82, -10, 285, 336, 90, 96, 0),
( 95, 62, 80, -20, 196, 239, 90, 98, 0),
( 96, 60, 80, 10, 95, 156, 90, 0, 94),
( 97, 60, 85, 30, 561, 622, 0, 0, 106),
( 98, 58, 75, 20, 30, 84, 90, 0, 95),
( 99, 55, 80, -20, 743, 820, 90, 100, 0),
( 100, 55, 85, 20, 647, 726, 90, 0, 99),
( 101, 25, 30, -10, 725, 786, 90, 51, 0),
( 102, 48, 30, -10, 632, 693, 90, 64, 0),
( 103, 28, 55, -10, 732, 777, 90, 23, 0),
( 104, 88, 35, -20, 109, 170, 90, 78, 0),
( 105, 5, 45, -10, 665, 716, 90, 36, 0),
( 106, 60, 85, -30, 561, 622, 90, 97, 0);
INSERT 0 106
The orders¶
The original data needs to be converted to an appropiate table:
WITH deliveries AS (SELECT * FROM lc101_c WHERE dindex = 0)
SELECT
row_number() over() AS id, p.demand,
p.id as p_node_id, p.x AS p_x, p.y AS p_y, p.open AS p_open, p.close as p_close, p.service as p_service,
d.id as d_node_id, d.x AS d_x, d.y AS d_y, d.open AS d_open, d.close as d_close, d.service as d_service
INTO c_lc101
FROM deliveries as d JOIN lc101_c as p ON (d.pindex = p.id);
SELECT 53
SELECT * FROM c_lc101 LIMIT 1;
id | demand | p_node_id | p_x | p_y | p_open | p_close | p_service | d_node_id | d_x | d_y | d_open | d_close | d_service
----+--------+-----------+-----+-----+--------+---------+-----------+-----------+-----+-----+--------+---------+-----------
1 | 10 | 3 | 42 | 66 | 65 | 146 | 90 | 75 | 45 | 65 | 997 | 1068 | 90
(1 row)