Benchmark of existing open source solutions for conflating structured, geographical and transit data

published on 21 April 2020 by Jungle Bus

More and more open transport data is available today. To take full advantage of this, the need for stable and durable identifiers is emerging: whether to recognize and deduplicate similar objects from multiple sources, to link or even enrich this data with other open data sets or to define complex fare agreements, it is necessary to be able to identify and retrieve an object with certainty through the available data sources.

But each transport operator and local authority has its own way of labeling and identifying its own data. In order to provide a unique and immutable identifier for each useful notion in the dataset, it will therefore be necessary to identify similar data across several datasets.
The following study is a comparative review of different open source conflation solutions for comparing and merging data.

General principles

The process of conflation can be explained as follows: it’s about comparing the data from one source (source A) with the data from another source (source B) according to a set of criteria to be finely defined. The output of this process is a segmentation into three sets of data :

The purpose is often to generate a merged dataset enhanced from both initial datasets, but depending on the situation several operations may be carried out with these datasets.

If there are two sets of data on the same perimeter, the focus will be on the data identified in both sets of data. The others will have to be monitored and will potentially indicate errors or gaps in the sources or conflating process.

If there are two partially overlapping datasets - for instance data from the same transport network provided by the operator (source A) and the transit authority (source B) that has a larger perimeter than the network - it is expected that a large portion of the dataset from source B will not be found in source A.

Finally, if there is an update of the same dataset by the same provider, we may for instance want to delete the data not found in the initial dataset and create the new dataset.

Tools studied

There are many open source conflation tools for OpenStreetMap. The common use case is to use a third party dataset (available under an open data license compatible with the OpenStreetMap license) in order to enhance the OpenStreetMap database.

After comparison, we want to identify

The Wikidata project, although more recently launched, also has many conflation tools that have a fairly similar working principle.
Finally, even if they do not have such a thriving open source ecosystem, we will study some conflation solutions specific to transport data.

We won’t study all the conflation tools of these ecosystems, but only the ones that matches our use cases:

Here is the list of the tools that will be reviewed:

Tool Ecosystem Object type
Conflation plugin for JOSM OSM Point
Osmose OSM Point
OSM Conflate OSM Point
OSM ↔ Wikidata OSM, Wikidata Point
Mix’n’match Wikidata Non geographic
OpenRefine Wikidata Non geographic
Ref-lignes-stif OSM, transport specific Transport objects
Tartare tools OSM, transport specific Transport objects
Go Sync OSM, transport specific Transport objects
gtfs2osm OSM, transport specific Transport objects

Conflating geographical points

Geographic points are elementary objects much used in cartography. Numerous open source tools have been developed to compare and facilitate the integration of point data in OpenStreetMap, from shops to various points of interest.

In our use case, we may want to use this tools to compare and match geographical points such as transport stops or subway entrances.

JOSM Conflation plugin

In JOSM, the main desktop application for editing OpenStreetMap data, a plugin that performs conflation is available.
It processes two input datasets:

Both datasets are in OpenStreetMap format (osm XML). The JOSM editor can load geojson and csv files and perform format conversion. But most of the time, adjustments have to be made in the reference dataset to match it with the data structure of OpenStreetMap.

For instance, we will transform:
file: stops.txt

stop_id stop_name stop_lat stop_lon location_type parent_station
NAQ:Quay:44930 Séreilhac - Les Betoulles 45.777938843 1.0951570272 0 NAQ:StopPlace:31168

file: stops.csv

highway name latitude longitude
bus_stop Séreilhac - Les Betoulles 45.777938843 1.0951570272

Several parameters should be set up to perform the data comparison:

Other advanced settings are also available for polygon comparison.

The output datasets are:

The source code is in Java and licensed under GPL v2 or later.


Osmose QA is a quality assurance tool for the OpenStreetMap database and it can also be used to match data from open data sources and update OpenStreetMap.

It takes as input a dataset (called “official”) in various formats (csv, geojson, GTFS stops.txt file, etc) and an OpenStreetMap database in .osm.pbf format.

You then need to define which OpenStreetMap objects are candidates for the match (OpenStreetMap type and set of tags) and to set up a conflation distance.
If there is an unique and stable id in the official dataset that can be added to OpenStreetMap, you can also use it for the conflation.

Then, you have to map the attributes of the official data to OpenStreetMap tags. Some are mandatory tags and some are secondary tags that are not checked on updates.

The conflation process can output various datasets:

Note that a same OpenStreetMap object can be in multiple outputs datasets: for instance, a valid existing object in OpenStreetMap without id will be in both “possible_merge” and “missing_official”.

Osmose is available “as a service” for OpenStreetMap contributors (as opposed to a tool for individual use) and performs periodic updates to reflect changes in the OpenStreetMap database and source dataset.

You can also export a merged dataset, containing the matched elements with OpenStreetMap attributes and location.

For example, this Osmose config file conflates train stations extracted from a GTFS feed to OpenStreetMap objects with railway=station or railway=halt tag.

    u"Horaires prévus des trains TER",
    GTFS(Source(attribution = u"SNCF", millesime = "08/2017",
            fileUrl = u"")),
    Load("stop_lon", "stop_lat",
        select = {"stop_id": "StopPoint:OCETrain%"}),
        select = Select(
            types = ["nodes", "ways"],
            tags = {"railway": ["station", "halt"]}),
        osmRef = "uic_ref",
        conflationDistance = 500,
        generate = Generate(
            static1 = {
                "railway": "station",
                "operator": "SNCF"},
            static2 = {"source": self.source},
            mapping1 = {"uic_ref": lambda res: res["stop_id"].split(":")[1][3:].split("-")[-1][:-1]},
            mapping2 = {"name": lambda res: res["stop_name"].replace("gare de ", "")},
            text = lambda tags, fields: {"en": fields["stop_name"][0].upper() + fields["stop_name"][1:]} )))

In this example, it uses as stable id:

For OpenStreetMap objects that does not have this tag, the distance used for the conflation is 500 meters.

Only the following outputs are proposed in Osmose web interface:

In Osmose web interface, you can review and edit each element to approve or amend the tag modifications that are suggested.

The source code is in Python and licensed under GPL v3.

OSM Conflate

OSM Conflate (also known as OSM Conflator) is a script to merge a dataset with OpenStreetMap data. It was developed by, a Russian company that develops the Maps.Me mobile application for travelers with offline maps and navigation from OpenStreetMap data. Its main purpose is to update already existing OpenStreetMap objects with attributes from the source dataset, and to create missing ones.

You need to define a profile to conflate the third-party dataset with OpenStreetMap, it can be a JSON file in simple cases or a python file. OpenStreetMap data can be provided (as osm XML file) or gathered remotely using Overpass API with a query set in the profile.

It can use a stable id (called dataset_id) to match objects from both datasets. If not set, it will find closest OpenStreetMap objects for each object from the dataset using the conflation distance set in the profile.

Its default output is an OSM change file with OpenStreetMap modifications that can be uploaded to OpenStreetMap with updated tags for the matched objects, new objects for the elements present only in the source dataset and remaining OpenStreetMap objects flagged with some special tag or deleted.

As OpenStreetMap community has a strict policy on data import from other sources and this use case is not recommended, you can also get a geojson output format to review the changes, with the following output datasets:

If the dataset_id was used, there are additional output datasets:

You can review this file on your own or use the web interface (OSM Conflator Audit System) to share the validation with the OpenStreetMap community.
It allows OpenStreetMap users to check each element, and change the proposed tags to be imported and location if needed. Once the whole dataset has been reviewed, the results can be imported back in OSM Conflate to create a new OSM change file.

The source code is in Python and licensed under Apache-2.0.

OSM ↔ Wikidata

OSM ↔ Wikidata is yet another enrichment tool for OpenStreetMap. It allows to add the wikidata tag on OpenStreetMap objects (which represents the id of the Wikidata item about the feature).
It is therefore a particularly interesting tool in the context of our study since it is about making a comparison between a structured base (Wikidata) and a geographical base (OpenStreetMap) in order to add an unique and stable identifier (the Wikidata identifier) on the objects of the geographical base.

The principle is to select objects in Wikidata and search for their equivalent in OpenStreetMap.
The algorithm is not based on a distance search but works by geographical area (for example a city or a neighborhood): all Wikidata objects with the property P625 (coordinate location) are retrieved in this area, then all OpenStreetMap candidate objects in this same area are retrieved. The matching is based on name (for OpenStreetMap) and English Wikipedia categories (for Wikidata objects).

We get as output:

If a match is found, the following attributes are compared and displayed for match candidates:

some match candidates

The source code is in Python and licensed under GPL v3.

Comparative review

All the conflation tools for geographical points studied here were from the OpenStreetMap ecosystem.
Their mechanism are quite similar :
You need to map your third-party dataset to the OpenStreetMap data model (create a geographical point with the coordinate and transform the attributes to OpenStreetMap tags)
Then, your conflation can be based on some unique and stable identifier that is both in the third-party dataset and in OpenStreetMap. You will use instead or in addition a conflation distance to find close elements to match.

Only OSM ↔ Wikidata tool has a different approach and try to maximize the number of OpenStreetMap candidates for each third party element to make sure you can find the appropriate match despite the differences in the data model.

Most of these tools needs you to investigate each element to validate the match and/or propose some kind of community review to go through the whole dataset. This is characteristic of the way OpenStreetMap works: the project has a large community and discourages massive data imports or modifications without reviewing every single item.

As a result, the visualization of the output is key to the success of the conflation and most tools displays the distance or the tags differences.
On a whole dataset point of view, OSM Conflate and the Conflation plugin offers the best visualization of the output datasets and are good options to iterate to find the best conflation distance.
Osmose interface is better on the element point of view, to check the tags to add, remove or update on OpenStreetMap object.

Most of these tools are designed for one-time integration. They can be used to perform update but it would be manual process to design. Only Osmose has some mechanism to periodically update both the third-party dataset and the OpenStreetMap data and automatically perform again the conflation. Its web interface even has graphical statistics about the evolution of each output dataset.

JOSM Conflation plugin Osmose OSM Conflate OSM ↔ Wikidata
need to match the dataset with OSM model yes yes yes no
use an identifier existing in both dataset possible, not mandatory possible, not mandatory possible, not mandatory possible, not mandatory
investigate each output element needed needed possible and recommended needed
collaborative review not possible yes possible yes
visualization of the conflation output ++ + ++ +
visualization of each output element + ++ + ++
language Java Python Python Python
user interface in JOSM dedicated web UI dedicated web UI web UI
license GPL v2 GPL v3 Apache-2.0 GPL v3

Conflating non geographical objects

Conflating non geographical objects consists mainly in comparing strings of characters, possibly standardized (without capital letters, accents, abbreviations, etc). The Wikidata integration tools are the ones that have taken this concept the furthest.

In our use case, we may want to use this tools to compare and match operators, networks or even transport lines or stations.


Mix’n’match is an online tool to match external datasets to Wikidata.

It takes some external dataset, call catalog, that contains a list of entries, with some unique id. The objective is to match each element with a Wikidata entry and to add the identifier of the catalogue to Wikidata. It can also create new elements for elements that don’t exist yet in Wikidata.

The conflation configuration does not need to implement mapping with Wikidata as it only relies on id and name. You can also define the Wikidata property for the catalog identifier and the default type of the objects in the catalog (is it about humans, books, train stations, etc)

A fuzzy match is performed on the name and the tool proposes the following outputs datasets that can be browsed by the users:

The users can then collaboratively approve or reject the proposed matches. An item can also me marked as irrelevant for Wikidata import (useful for duplicates for instance).

In the end, there are five outputs datasets:

Current status of the import of the UIC catalog (that contains European train stations)

There is also a game mode where the user is presented to a random element not matched yet, with the search results from both Wikidata and Wikipedia projects and has to choose the most relevant item for matching. Once chosen, the identifier is added to Wikidata and another item is presented for review.

Here, we are matching the UIC catalog to Wikidata in game mode. The current element “Delitzsch ob Bf” seems to be matching with “Delitzsch oberer Bahnhof” that already exists in Wikidata and has a page in

Mix’n’Match source code is in PHP and licensed under GPL v3.

OpenRefine & Reconcile-csv plugin for OpenRefine

OpenRefine is a web desktop application to clean up messy dataset and perform format conversion. It is widely used to prepare data import for Wikidata.

OpenRefine’s reconciliation service can be used to conflate non geographical objects. The service will take a dataset of elements with some text (a name or a label) and return a ranked list of potential objects matching the criteria from another remote database.
By default, this remote database is Wikidata, but other databases can be added if they provide a compatible API (ORCID, Open Library, etc).
With the reconcile-csv plugin for OpenRefine, you can even use a csv file as external database and use fuzzy matching on the labels.

You can improve the conflation process by providing additional properties to narrow down the research. For instance, when conflating a database of books, the author name or the publication date are useful bits of information that can be transferred to the reconciliation service. If your database contains a unique identifier stored in Wikidata, you can also use it in the conflation process.

conflating subway lines from OpenStreetMap with Wikidata and match some tags to Wikidata properties to narrow down the search

As a result, you get several matching candidates for each item, with a score: the likelihood the two compared elements are the same.
You can then review each element, filter by score or automatically match each element with its best match.
You can also configure complex workflow to enhance your initial dataset with attributes from the remote dataset.

matching some subway lines from OpenStreetMap with Wikidata in OpenRefine

OpenRefine source code is in Java and licensed under BSD 3-Clause “Revised” License. Reconcile-csv source code is also in Java and released under BSD 2-Clause “Simplified” License.

Comparative review

Unlike previous tools, which required precise mapping between attributes, these tools require much less configuration and iteration. They perform fuzzy name searches, taking great advantage of the ability to define aliases for labels in the Wikidata database.

We find again the possibility to use an identifier for the matching (the Wikidata identifier).

These tools also follow the paradigm of proposing several candidates if possible rather than choosing the one that corresponds the most, leaving the user to make the final decision.

Mix’n’Match OpenRefine & Reconcile-csv plugin for OpenRefine
need to match the dataset with OSM model partially partially
use an identifier present in both dataset yes possible, not mandatory
investigate each output element needed possible
collaborative review yes not possible
visualization of the conflation output ++ -
visualization of each output element ++ +
language PHP Java
user interface web web
license GPL v3 BSD

Conflating transport data

Conflating transport data is about conflating both stops and routes, by using the logical and hierarchical structuring of transport data to improve the conflation process.

Most of the tools come from the OpenStreetMap ecosystem and aim to use open data transport in GTFS format to update OpenStreetMap.

GO Sync

GO Sync (also called GTFS OSM Sync) is a desktop application to synchronize the bus stop and route information from a GTFS dataset with OpenStreetMap. It creates stop and route data in OpenStreetMap using GTFS information. It was created in 2010 and uses a simplified data model for OpenStreetMap route that is not in use anymore. It also only works for buses.

It can be used both for initial data creation in OpenStreetMap and for updating.

It starts by conflating stops using

Its output is segmented into four different groups:

The user can then review the results and add or update bus stop data to OpenStreetMap, using GTFS attributes.

It also has a route conflation tool, that match GTFS route with OpenStreetMap route relation (that is more of a line variant in the latest data OpenStreetMap data model).
To conflate the route, it will use:

It will output four datasets just like for the stops. If no route is found, it can be created using the GTFS attributes and the existing OpenStreetMap matched stops as members.


GO Sync source code is in Java and licensed under Apache-2.0.

Another more recent tool in the OpenStreetMap ecosystem exists with similar use cases: GTFS Integrate.
It allows to precisely map the GTFS attributes to OpenStreetMap ones in order to prepare a data integration. It does not conflate much but is focused on initializing OpenStreetMap data structures using available information.


gtfs2osm is a web-based tool for to create transport data in OpenStreetMap using a GTFS. It has therefore some functions to compare the GTFS with the already existing OpenStreetMap data in order to avoid the creation of duplicates objects.

It is in the form of a script that creates web pages by recursively browsing GTFS objects. It takes as input a GTFS file and an OpenStreetMap database in osm.pbf format. We will look at the original version (originally thought for the Paris region) as well as a more recent version (thought for Luxembourg), although there is not really any specificity for some geographical area or network in this tool.

The website proposes to explore the GTFS by first choosing a mode of transport (GTFS route_type) among those available, then a transport agency, then a line (GTFS route), then a line variant (GTFS trip, knowing that the trips entirely included in another are filtered out). It is on this line variant page that the use really begins.

For each GTFS stop on this trip, several OpenStreetMap candidates objects are explored by the script, which will finally choose the closest OpenStreetMap stop, or a nearby stop with exactly the same name, or nothing if no candidates are found.
The search for candidate OpenStreetMap objects is based on several tags chosen according to the mode of transport, in order to take into account the diversity of contributions in OpenStreetMap (where several transport models coexist)

Only one candidate is presented, with information on the distance and similarity of the name. The user can then import this object into JOSM OpenStreetMap editor, and automatically add an additional number of tags.

The user can also create in one click in JOSM the OpenStreetMap object corresponding to the trip (a route relation) based on the information present in the GTFS.

Going up in the breadcrumb trail, the user is returned to the line page, where they are shown the OpenStreetMap objects that could match (route_master relations) with a matching mode of transport, line number and transport network, or with a matching identifier present in both OpenStreetMap and GTFS. If no candidate is found, the user can create the OpenStreetMap object in JOSM in one click using the information from the GTFS.

gtfs2osm source code is in Perl and licensed under GPL v3.


ref-lignes-stif (also called STIF-to-OSM) is a web interface to compare and add unique identifier to OpenStreetMap transport objects in Île-de-France (Paris region).

The local authority of the Paris region (who was called STIF when the tool was initially released) has defined unique identifiers for each public transport line (~ 1800) and stop (~40 000). The tool helps OSM contributors to add these identifiers to OpenStreetMap objects in a guided workflow: matching lines, then line variants, then stops. It is a web tool and it uses Overpass web service to query OpenStreetMap and API to get public transport objects.

The entry point is a searchable list of lines from OpenStreetMap. When you select a line, the tools presents you open data candidates based on the line number. For the 91-10 bus line, you will only get one candidate while for a bus line labelled “2” you will get 19 results to review.
For each candidate, you can review its attributes, its shape and its stops from both OpenStreetMap et Paris region open data.

The user has to choose the best match within the proposals: the open data line identifier will be retrieved and added to OpenStreetMap, and the user will access the line variant review page.

On this page, you get the list of the line variants from OpenStreetMap and you have to choose between the line variants from the open dataset. In most cases, in both OpenStreetMap and the open dataset, the line variants have their origin and destination as labels, so you can easily choose the good one. A debug page is available to see the shapes and stops from OpenStreetMap and the stops from the open dataset for each line variant to help the user to make the right choice.
As there is no line variant identifier in the open dataset, no identifier is added to OpenStreetMap, but the user can then access the stop review page.

On the stop review page, you get the list of the stops of the chosen line variant, and for each one, the closest open data stop which is served by the open data line variant previously chosen. You can compare their names and distance and automatically retrieve and add the open data identifier in OpenStreetMap to the stop.

The web tool comes along with debug tools for each step:

The source code is in Javascript and released under the MIT license.


Tartare-tools is a library to read transport data and perform format conversion. It also has some functions to improve data using OpenStreetMap data. It was developed by Kisio Digital, a French company that provides digital services to simplify travel for passengers.

It takes as input a transport dataset in a custom format and an OpenStreetMap database in .osm.pbf format. It can

The library has a function to automatically match the transport objects (lines, line variants and stops) to OpenStreetMap objects but you can also provide some or all matchings to use the enhancements functions.

You need to provide as configuration the matching between the transport dataset network and OpenStreetMap network.
The library will first match OpenStreetMap lines (as route_master relation objects) using:

If there is only one candidate, it will then match the line variants (called route in the library, as in OpenStreetMap) with OpenStreetMap line variant (as route relation objects) using:

If there is only one candidate, it will then match the stops of the line variant with OpenStreetMap stops using the name. As for other objects, the matching is valid only if there is only one candidate.

The library is designed to require very little configuration (only network matching) and has very strict conditions to limit false positives. It is therefore not necessarily intended to match the entirety of the objects. The number of objects actually matched will depend on the quality of the OpenStreetMap data in terms of conformity to the model, completeness and uniformity of the attributes filled in.

Tartare-tools source code is in Rust and licensed under GPL v3.

Other approaches


Sophox is a web tool that allows to query OpenStreetMap data, some OpenStreetMap metadata and Wikidata data at the same time. It stores all these data in a structured semantic way, using subject predicate object statements.

By doing cross queries, it allows, for example, to find similar objects in OpenStreetMap and Wikidata using a common identifier (the Wikidata identifier) and to compare their attributes. For example, here is a query comparing the attributes of subway lines:

PREFIX wd: <>
PREFIX wdt: <>
PREFIX rdfs: <>
PREFIX hint: <>

SELECT DISTINCT ?wikidata_id ?osm_id ?label ?wikidata_color ?osm_color ?wikidata_operatorlabel ?osm_operator ?wikidata_networklabel ?osm_network WHERE {
  hint:Query hint:optimizer "None".
  SERVICE <> {
    ?wikidata_id wdt:P31 wd:Q15079663.
    ?wikidata_id wdt:P17 wd:Q142.
    ?wikidata_id rdfs:label ?label.
    OPTIONAL { ?wikidata_id wdt:P465 ?wikidata_color. }
      ?wikidata_id wdt:P137 ?wikidata_operator.
      ?wikidata_operator rdfs:label ?wikidata_operatorlabel.
      FILTER((LANG(?wikidata_operatorlabel)) = "fr")
      ?wikidata_id wdt:P361 ?wikidata_network.
      ?wikidata_network rdfs:label ?wikidata_networklabel.
      FILTER((LANG(?wikidata_networklabel)) = "fr")
    FILTER((LANG(?label)) = "fr")
    ?osm_id osmt:wikidata ?wikidata_id.
    ?osm_id osmm:type "r".
    ?osm_id osmt:type "route_master".
    ?osm_id osmt:operator ?osm_operator.
    ?osm_id osmt:network ?osm_network.
    ?osm_id osmt:colour ?osm_color.

Using this approach for conflation seems complex, but it could help identify errors or generate new composite datasets mixing sources.

Sophox source code is licensed under Apache-2.0.

Transitland onestop ID

Transitland is a community edited website and webservices aggregating transport databases. To link datasets, operators, stops and routes across disparate sources, it uses a mechanism called “Onestop ID scheme”.

This mechanism consists of assigning a unique, perennial and readable identifier, independent of the identifiers already present in the source data. It is mainly based on a geohash and an abbreviated name and is not really designed to effectively compare and merge datasets.

Map matching tools

In the context of comparing and improving data from different sources, the practice of map matching is also sometimes used.
This generally involves bringing together data of quite different natures (usually a series of geographic coordinates and a road or rail network). For example, several tools attempt to use the OpenStreetMap road network to complete transport routes independently of the structured transport data in OpenStreetMap.

The objectives are diverse but quite different from our use case:

ref-lignes-stif can use the stops sequence from a GTFS route to compute a shape, and then map match it with OpenStreetMap bus road network to help OpenStreetMap contributors to create the shape in OpenStreetMap

Comparative review

There are two kinds of tools that use the logical and hierarchical structuring of transport data to improve the conflation process:

Those who start with stops (gtfs2osm, GO Sync, GTFS Integrate) match the stops from both sources using the methods described in the “conflating geographical points” paragraph above. Then they create the routes and lines if necessary using methods similar to those described in the “Conflating non geographical objects” paragraph.

The use case here is more focused on creating data in OpenStreetMap than on conflation. The main interest of these tools is in the ability to initialize OpenStreetMap objects using GTFS attributes and without necessarily having a deep knowledge of the GTFS format.
Starting with the stops is also a form of prerequisite because the stops are needed to create the objects representing the lines in OpenStreetMap.

In contrast, those that start with lines are more focused on conflation than on creation of missing data.
The main interest of this approach is for bus stops, where associating stops on either side of the road (which have a close distance and similar attributes) is unefficient with traditional point conflation approaches. By using the line variants that serve these stops, the risk of mismatching is therefore reduced.

The two tools studied have different approaches: Tartare-tools will rather minimize the risk of false positive, even if it means not proposing an association at all, while ref-lignes-stif will maximize the number of candidates proposed for each association in order to be able to find a match for each object, even if it means having the user make all the choices and spend a lot of time at it.

In any case, these tools offer interesting insights on visualizing transport data from various sources.

GO Sync gtfs2osm ref-lignes-stif Tartare-tools
conflation process stops > routes stops > line variants > lines line > line variants > stops networks > lines > line variants > stops
need to match the dataset with OSM model yes no no partially
use an identifier present in both dataset yes possible yes not possible
investigate each output element yes yes needed no
collaborative review no yes yes not possible
visualization of the conflation output - + - no
visualization of each output element + + ++ no
language Java Perl Javascript Rust
user interface desktop application web web no
license Apache-2.0 GPL v3 MIT GPL v3


There are well-established mechanisms for performing data conflation:

In the case of transport data, using the transport model hierarchy greatly improves the quality of the process by limiting false positives, but may be sensitive to modeling discrepancies.

Using an identifier to link datasets is a proven technique found almost systematically. It is also a good solution for maintenance over time, which is critical for a database edited collaboratively by several actors and very relevant in the context of evolving transport data.

While many tools exist in OpenStreetMap and Wikidata collaborative databases ecosystems, no single tool can meet all needs, due to the diversity of use cases.

The main choice to be made according to the use case will be to propose several candidates for each object (which implies a more or less long and tedious work of reviewing each element) or, conversely, to assign the best candidate to each object.
A review may remain proposed in any cases, and may be collaborative or not.

Whatever the algorithm and process, special attention must be paid to the visualization of the results, whether it is the individual debugging of the matching of one element, or the global visualization of the results and the percentage of objects in each category.


This study was carried out by Jungle Bus for MobilityData as part of the Mobility Database project.

This document is made available under the conditions of the CC-BY-SA license.


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