openlr-dereferencer 0.9.2

OpenLR location dereferencer

openlr-dereferencer

This is a Python package for decoding OpenLR™ location references on target maps.

Dependencies

• Python ≥ 3.6
• geographiclib (PyPi package)
• openlr (PyPi package)
• For unittests, SQlite with spatialite extension is required

State

• ☑ Example map format
• ☑ Routing
• ☑ Candidate route rating
• ☑ Backtracking to get correct routes
• ☑ Decoding line locations
• ☑ Decoding 'point along line' locations
• ☐ Decoding 'POI with access point' locations

Structure

It is divided into the following submodules:

maps

Contains an abstract map class, which you may want to implement for your target map.

maps.wgs84 provides methods for reckoning with WGS84 coordinates.

example_sqlite_map

Implements the abstract map class for the example map format used in the unittests and examples

decoding

The actual logic for matching references onto a map.

This includes finding candidate lines and scoring them, and assembling a dereferenced location.

Usage

The decode(reference, mapreader) function will take a location reference and return map objects.

Usage Example

First, implement the MapReader class for your map. The example_sqlite_map module is an implementation you may look at.

Second, construct a location reference. For instance, parse an OpenLR line location string:

from openlr import binary_decode
reference = binary_decode("CwmG8yVjzCq0Dfzr/gErRRs=")

Third, decode the reference on an instance of your map reader class:

from openlr_dereferencer import decode
real_location = decode(reference, mapreader)

real_location.lines # <- A list of map objects

Configuration

Candidates

The configuration value openlr_dereferencer.SEARCH_RADIUS determines how far away from the LRP road candidates are searched. The unit is meters, the default 100.

Scores

Every candidate line gets a score from 0 (bad) to 1 (perfect).

There are four scoring weight parameters:

• GEO_WEIGHT = 0.25
• FRC_WEIGHT = 0.25
• FOW_WEIGHT = 0.25
• BEAR_WEIGHT = 0.25

They determine how much influence a single aspect has on an overall candidate's score.

You may just change them before decoding:

from openlr_dereferencer.decoding import scoring

scoring.GEO_WEIGHT = 0.66
scoring.FRC_WEIGHT = 0.17
scoring.FOW_WEIGHT = 0.17
scoring.BEAR_WEIGHT = 0

Logging

openlr-dereferencer logs all mapmatching decisions using the standard library logging module.

Use it to turn on debugging:

import logging

logging.basicConfig(level=logging.DEBUG)