Shortest Path with Turn Penalties & Restrictions

The following is a complete example, using the Python API, of solving a graph created with a Washington, D.C. HERE dataset for a shortest path problem with turn penalties via the /solve/graph endpoint. For more information on Graphs & Solvers, see Graphs & Solvers Concepts. For more information on turn penalties and restrictions, see Using Turn-based Weights & Restrictions.

Prerequisites

The prerequisites for running this solve graph example are listed below:

Graph server enabled
Python API
Solve graph script
D.C. shape CSV file

Python API Installation

Depending on the target operating system, a Python virtual environment may need to be installed first:

Python Virtual Environment

The native Kinetica Python API is accessible through the following means:

PyPI
Git

Python Virtual Environment

A Python virtual environment is necessary to install in an operating environment where Python is externally managed.

Install a Python virtual environment:
```
python3 -m venv .venv
```
Activate the Python virtual environment:
```
source .venv/bin/activate
```

PyPI

Install the API:
```
pip3 install gpudb
```
Test the installation:
```
python3 -c "import gpudb;print('Import Successful')"
```
If Import Successful is displayed, the API has been installed as is ready for use.

Git

In the desired directory, run the following, but be sure to replace <kinetica-version> with the name of the installed Kinetica version, e.g., v7.2:
```
git clone -b release/<kinetica-version> --single-branch https://github.com/kineticadb/kinetica-api-python.git
```
Change directory into the newly downloaded repository:
```
cd kinetica-api-python
```
In the root directory of the unzipped repository, install the Kinetica API:
```
sudo pip3 install .
```
Test the installation (Python3 is necessary for running the API example):
```
python3 examples/example.py
```

Data File

The example script references the dc_shape.csv data file, mentioned in the Prerequisites, in the current local directory, by default. This directory can specified as a parameter when running the script. The dc_shape dataset is a HERE dataset and is analogous to most road network datasets you could find in that it includes columns for the type of road, the average speed, the direction of the road, a WKT linestring for its geographic location, a unique ID integer for the road, and more. The graph used in the example is created with four columns from the dc_shape dataset:

link_id — a sharded integer column composed of unique IDs for each road segment mapped within this dataset
shape — a WKT linestring composed of points that make up various streets, roads, highways, alleyways, and footpaths throughout Washington, D.C.; this column is also part of an inline calculation for distance as weight during graph creation
direction — an integer column that conveys information about the directionality of the road, with forward meaning the direction in which the way is drawn in OSM and backward being the opposite direction:
- 0 — a forward one-way road
- 1 — a two-way road
- 2 — a backward one-way road
speed — an integer column that represents the average speed traveled on a given road segment; this column is also part of an inline calculation for distance as weight during graph creation

Script Detail

This example is going to demonstrate solving for the shortest path with varying turn-based penalties and restrictions between source points and destination points located within a road network in Washington, D.C.

Constants

Several constants are defined at the beginning of the script:

SCHEMA — the name of the schema in which the tables supporting the graph creation and solve operations will be created
The schema is created during the table setup portion of the script because the schema must exist prior to creating the tables that will later support the graph creation and match operations.
TABLE_DC — the name of the table into which the D.C. road network dataset is loaded
GRAPH_DC — the D.C. road network graph
SOLUTION_GRAPH_DC_1 / SOLUTION_GRAPH_DC_1 / SOLUTION_GRAPH_DC_3 — the D.C. road network graph shortest path solution tables

Constant Definitions

SCHEMA = "graph_s_dc_shortest_path_turn"
TABLE_DC = SCHEMA + ".dc_shape"

GRAPH_DC = SCHEMA + ".dc_shape_graph"
SOLUTION_GRAPH_DC_1 = GRAPH_DC + "_solved_sp"
SOLUTION_GRAPH_DC_2 = GRAPH_DC + "_solved_sp_intersection-penalty"
SOLUTION_GRAPH_DC_3 = GRAPH_DC + "_solved_sp_turn-restriction"

Table Setup

Before the graph can be created, the D.C. shape dataset is loaded from a local CSV file. First, the D.C. shape table is created using the GPUdbTable interface:

Create D.C. Shape Table

table_dc_obj = gpudb.GPUdbTable(
    _type = [
        ["link_id", "long", "shard_key"],
        ["direction", "int"],
        ["speed", "double"],
        ["road_type", "string", "char1"],
        ["seg", "string", "wkt"],
        ["shape", "string", "wkt"],
    ],
    name = TABLE_DC,
    db = kinetica,
    options = {}
)

Then, the CSV file is uploaded into KiFS and loaded into the table:

Populate D.C. Shape Table

kinetica.create_directory("data", {"no_error_if_exists":"true"});
kinetica.upload_files("/data/" + CSV_FILE, open(csv_path, "rb").read())
kinetica.insert_records_from_files(TABLE_DC, ["kifs://data/" + CSV_FILE])

Graph Creation

One graph is used for the shortest path solve graph example utilized in the script: GRAPH_DC, a graph based on the dc_shape dataset (the CSV file mentioned in Prerequisites). The GRAPH_DC graph is created with the following characteristics:

It is directed because the roads in the graph have directionality (one-way and two-way roads)
It has no explicitly defined nodes because the example relies on implicit nodes attached to the defined edges
The edges are identified by WKTLINE, using from WKT LINESTRINGs from the shape column of the dc_shape table. Each road segment’s directionality (DIRECTION) is derived from the direction column of the dc_shape table. Each edge is associated with an ID from the link_id column. Rather than separately define weights, each edge has its weight (WEIGHT_VALUESPECIFIED) calculated inline as the length of the entire shape column’s LINESTRING (in meters) divided by the number of points in the LINESTRING minus 1 divided by the average speed used to traverse the LINESTRING.
It has no inherent restrictions for any of the edges in the graph
It utilizes the following options:
- It will be replaced with this instance of the graph if a graph of the same name exists (recreate)
- The graph will register dummy edges around each intersection of edges generated from the data to represent turns (add_turns)

Create D.C. Shape Graph

cg = kinetica.create_graph(
    graph_name = GRAPH_DC,
    directed_graph = True,
    nodes = [],
    edges = [
        TABLE_DC + ".link_id AS ID",
        TABLE_DC + ".shape AS WKTLINE",
        TABLE_DC + ".direction AS DIRECTION",
        "(ST_LENGTH(" + TABLE_DC + ".shape,1)/"
        "(ST_NPOINTS(" + TABLE_DC + ".shape)-1))/" +
        TABLE_DC + ".speed AS WEIGHT_VALUESPECIFIED"
    ],
    weights = [],
    restrictions = [],
    options = {
        "recreate": "true",
        "add_turns": "true"
    }
)

Shortest Path

No Turn Penalties or Restrictions

The first example illustrates a simple shortest path solve from a single source node to a single destination node. First, the source node and destination node are defined.

The source and destination node are the same for each example.

Define Beginning & Ending Points

src = ["POINT(-77.04489135742188 38.91112899780273)"]
dest = ["POINT(-77.03193664550781 38.91188049316406)"]

Next, the GRAPH_DC graph is solved with the solve results being exported to the response:

Solve Graph for Shortest Path without Penalties or Restrictions

kinetica.solve_graph(
    graph_name = GRAPH_DC,
    solver_type = "SHORTEST_PATH",
    source_nodes = src,
    destination_nodes = dest,
    solution_table = SOLUTION_GRAPH_DC_1
)

The cost for the source node to visit the destination node is represented as time in seconds:

Solve Graph for Shortest Path without Penalties or Restrictions Solution

+---------------------+
|   Cost (in seconds) |
|---------------------|
|             106.994 |
+---------------------+

The solution output to WMS:

Intersection Penalty

The second example illustrates a shortest path solve from a single source node to a single destination node, but this time traveling through an intersection of two-way roads will incur an additional cost. The GRAPH_DC graph is solved with the solve results being exported to the response and an intersection penalty of 20 seconds:

Solve Graph for Shortest Path with Intersection Penalties

kinetica.solve_graph(
    graph_name = GRAPH_DC,
    solver_type = "SHORTEST_PATH",
    source_nodes = src,
    destination_nodes = dest,
    solution_table = SOLUTION_GRAPH_DC_2,
    options = {
        "intersection_penalty": "20"
    }
)

The cost for the source node to visit the destination node is represented as time in seconds:

Solve Graph for Shortest Path with Intersection Penalties Solution

+---------------------+
|   Cost (in seconds) |
|---------------------|
|             135.799 |
+---------------------+

The solution output to WMS, noting the immediate U-turn to return to Q Street from Connecticut Avenue to avoid the intersection penalty:

Turn Restriction

The third example illustrates a shortest path solve from a single source node to a single destination node, but this time a single turn from Q Street on to 15th Street is restricted. The GRAPH_DC graph is solved with turns from road segment ID 18352169 (Q Street) to road segment ID 18352166 (15th Street) being restricted and the solve results being exported to the response:

Solve Graph for Shortest Path with Turn Restrictions

kinetica.solve_graph(
    graph_name = GRAPH_DC,
    restrictions = [
        "{18352169} AS FROM_EDGE_ID",
        "{18352166} AS TO_EDGE_ID",
        "{0} AS ONOFFCOMPARED"
    ],
    solver_type = "SHORTEST_PATH",
    source_nodes = src,
    destination_nodes = dest,
    solution_table = SOLUTION_GRAPH_DC_3
)

The cost for the source node to visit the destination node is represented as time in seconds:

Solve Graph for Shortest Path with Turn Restrictions Solution

+---------------------+
|   Cost (in seconds) |
|---------------------|
|             108.284 |
+---------------------+

The solution output to WMS, noting the detour on to 16th Street and Church Street to avoid the restricted turn:

Download & Run

Included below is a complete example containing all the above requests, the data files, and output.

To run the complete sample, ensure that:

the solve_graph_dc_shortest_path_turn.py script is in the current directory
the dc_shape.csv file is in the current directory or use the data_dir parameter to specify the local directory containing it

Then, run the following:

Run Example

python solve_graph_dc_shortest_path_turn.py [--url <kinetica_url>] --username <username> --password <password> [--data_dir <data_file_directory>]

​Prerequisites

​Python API Installation

​Python Virtual Environment

​PyPI

​Git

​Data File

​Script Detail

​Constants

​Table Setup

​Graph Creation

​Shortest Path

​No Turn Penalties or Restrictions

​Intersection Penalty

​Turn Restriction

​Download & Run

Prerequisites

Python API Installation

Python Virtual Environment

PyPI

Git

Data File

Script Detail

Constants

Table Setup

Graph Creation

Shortest Path

No Turn Penalties or Restrictions

Intersection Penalty

Turn Restriction

Download & Run