RUBI's database of Railway-Projects in Switzerland
The database aggregates data from various sources and allows for detailed analysis. It offers you intelligence about futures projects and their location. Using those results you get a solid foundation for your predictions including to-be-opened tenders, projected resource requirements and expected rail-related projects.
Furthermore, we monitor the development of various buzzwords such as BIM, maintenance, reconstruction, catenary, etc. in different areas as well as the latest maintenance data of various track sections for forecasts of future railroad projects.
Get full access to the data with an annual subscription. Contact us.
The darker pink represents projects from SIMAP, the lighter pink represents projects from planning approval procedures.
Evaluation table
This table summarizes all SIMAP awards of contract since 1 January 2021.
Overview Technologies
Via SIMAP's SOAP API we can obtain any project and tender data. We have various filters available at their endpoint as well as our custom-made ones.
From the XML data we extract relevant information and write it to our local Neo4j graph database. We can thus analyze a wide variety of links and relationships: Geographically, systematically and across the board.
The insights gained in this way are made available to you via the Django framework from our web server directly on the Internet. This allows us to quickly create professional dashboards for your desired insights.
On the leaflet map, the project distribution over all operating points in Switzerland can be viewed for different time periods.
Graph database
In a Neo4j database, we mapped the route network of Switzerland as a graph with operating points and network segments.
These have geographical information via coordinates/routes which are enriched with project data from SIMAP and the PGVs.
They can be read out via Python in the Django frontend. Thus, live dashboards with the latest relevant awards and tenders can be created and automatic extracts/notifications can be set up.
Data model
The data model is extremely flexible and becomes more sophisticated with each new use case, which in turn allows more opportunities to gain intelligence from the model.
New data sources are incorporated into the existing model to increase data density and improve the quality of our conclusions.
Visualization
Shown here is a leaflet map with a background from Swiss-Topo for geographic assessment of the latest projects.
Other evaluation options include statistics on competitors, intelligent suggestions for search terms and development analysis.
CI/CD pipeline
The CI/CD pipeline is triggered by a contribution of source code (Commit) to the version control system Git. The following steps are carried out automatically on the server side:
COMMIT --> BUILD --> TEST --> DEPLOY
Build: Retrieve third-party libraries as a basis, build the software from source code
Test: Testing the software using tests predefined in the source code
Deploy: Installing the software on a test or production system
All work steps are logged and can be traced at a later date. This level of automation enables development steps and changes to be implemented with high frequency and quality.
