Today’s fragmented railroad framework siloed by profession raises several issues: On the one hand, a fragmented framework meets the needs of players on-site but does not give infrastructure managers an overarching vision of their assets. On the other, a framework split into silos restricts itself to databases designed for use by only one rail department (e.g. maintenance or engineering). Without pooled data, the industry is fated to a life of useless data duplication and erroneous legacy updates. The solution: a unified railroad framework.
As things are today, siloed frameworks are preventing updates from being properly deployed, as fragmented frameworks provide knowledge of legacy equipment only at a local level. To increase quality and promote real-time data updates, a unified railroad framework is needed. Yet and still, digitization in the rail industry can only take place if field-level sensors, digital simulation, and predictive maintenance are implemented. For such features to exist, an exhaustive, up-to-date railroad data framework must first come to the fore.
◇ Anatomy of a rail network: Why and how to unify it? ◇
Unifying the framework doesn’t necessarily mean building a unique, centralized, monolithic system designed as an infrastructure data vacuum. On the contrary, such a system would quickly become difficult to maintain or modify and consumers would lose interest in it.
Defining a unified railroad framework means creating a single source of truth—or repository—for each data type. It also means forbidding data duplication, promoting references, and requiring each object to have a unique identifier. By relying on these principles, the fog seems to lift on a unified rail framework running on a distributed, agile system.
In the rail sector, the goal of digitization is to increase the added value of rail infrastructures. For this challenge to be met, a solid framework is necessary before use cases can be massively created. Rail digitization makes a number of promises: self-driving trains, more efficient preventive maintenance, and a Pan-European train control system.
For self-driving trains to operate, they must have information about the infrastructure they’re operating on. This includes what devices they will interact with, physical characteristics (slopes and cants) and operational notions on the track (speed limits), not to mention all the other devices that cross their paths (beacons, signals, railroad crossings). Maps must be exhaustive, shared, and updated in real-time. Without a unified data repository, the automatic train is merely a fantasy.
Artificial intelligence analyzes data streams, crossing measures taken by trains with recorded catenary mechanical stress to find correlations between certain events and breakdowns. So long as the rail framework is not unified, it will be impossible to draw conclusions from such measures, which brings to light yet another problem: Why invest massively in data sensors if the benefits are non-existent or can’t serve the community?
The standard defined by the UIC (International Union of Railways) is deployed by all infrastructure managers. It will eventually replace fixed communication with train-to-ground communication. Once this occurs, the UIC hopes to standardize train traffic at a European level and improve line capacity by conciliating trains.
To adequately deploy ETCS, the first step is to up the quality of framework and, consequently, the precision of sources (drones, LIDAR, etc).
From an information systems standpoint, a unified rail framework is nothing to scoff at, since infrastructure managers will be relying on the standards. Once implemented by software publishers and companies, such standards will allow costs to be pooled, reducing the integration costs of a centralized data repository. Eulynx, RailTopoModel, and IFC Rail are just a few of the unified railroad framework adopted by GiSmartware to meet the needs of train network managers. To learn more about these standards, take a peek at our interview with Fabrice Simonin, Head of the Rail Division at GiSmartware!
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