ArcGIS Railroads Data Model - New!
The availability of a practical and up-to-date data model template, tailored to the unique needs of an industry, is one of the keys to a successful GIS implementation, whether one’s use of Esri software is new or long-standing.
For this reason, Esri collaborates with industry and academic leaders to continually evolve a range of geodatabase data model templates. The intent of Esri data model templates is to provide users with a best practice, industry-specific starting point.
Most users start with these data model templates; then they refine and extend them to meet their specific needs and requirements. Esri data model templates work with the ArcGIS platform and reflect Esri’s view of best data model practice.
The ArcGIS Railroads Data Model is a geodatabase data model template for organizations that operate in the railroad industry, or in industries that operate with railroad-based operations and assets. It is a moderately normalized data model, intended to digitally represent physical and non-physical aspects of railroad operations.
Esri thanks all those professionals and organizations who contributed their time and talents to the creation and improvement of the ArcGIS Railroads Data Model for the good of the community and of all Esri users in the Railroad industries.
Click here to DOWNLOAD the data model and all supporting materials.
Goal of the ArcGIS Railroads Data Model
The goal of the ArcGIS Railroads Data Model is to make it easier, quicker, and more cost-effective for Railroad organizations to implement the ArcGIS system. The Esri provided data model template accomplishes this by freely providing a data model that takes full advantage of the capabilities of the geodatabase. The data model is created and tested with ArcGIS products to ensure that it works. This significantly reduces the complexity, time, and cost to implement a spatially enabled Railroads data repository.
Keeping up with the advancements of the geodatabase is an ongoing activity. Esri software development staff continue to enhance and evolve the capabilities of the geodatabase. In addition to the data model representing a best practice on how to leverage the geodatabase, the data model also represents a repository of industry knowledge. Much of the structure and content of this data model is based on feedback from Esri’s users as well as lessons learned from the large number of implementations of ArcGIS in the railroads industries.
Because the ArcGIS Railroads Data Model is built specifically for the ArcGIS family of products, it can be implemented as-is, without modification to ArcGIS products.
Audience
At this 2024 edition of the data model, the primary target audience are freight railroad organizations across North America.
Passenger rail organizations in North America, as well as all kinds of rail organizations in the world outside of North America have many significant differences in their assets, operations, as well as requirements and constraints that are specific to different countries and regions. That isn’t to say that for those other organizations this data model would be useless. These other kinds of rail organizations may find valuable use from this data model as a starting point, or as a resource for enhancing their own database design.
It is simply important to recognize that for organizations other than North American railroads, this version of the data model has not comprehensively considered their needs. We welcome partners and other organizations would would like to collaborate with Esri for improving and extending this data model further, to broaded its scope for future versions.
Design Considerations
The ArcGIS Railroads Data Model has two sets of design considerations:
1. INDUSTRY COMPATIBILITY -- This data model needs to be fully compatible with other industry standard data models that already exist and are in productive use across the railroad industry. This is important for both interoperability purposes as well as making data exchange and translation easier. To that end, this Esri Railroad Data Model includes elements of, and is fully compatible with:
a. North American Rail Network (NARN) database model: Maintained and published by the US Department of Transportation’s Federal Railroad Administration.
b. Positive Train Control (PTC) database model: This is a set of technologies implemented to prevent some of the most major human-error incidents such as train-to-train collisions, over-speed derailments, incursions into established work zone limits, and the movement of trains thru a mainline switch left in the wrong position. PTC accomplishes these objectives in part with a database model for storing and updating data (including spatial data) in a standard and useful way. This database model is also governed by the US Department of Transportation’s Federal Railroad Administration.
c. Rail Industry Geographic Information System (RIGIS™) database model: Maintained and published by Railinc, which is a wholly owned, for-profit subsidiary of the Association of American Railroads (AAR). Railinc is a resource for technology solutions used by railroads across North America. RIGIS™ is the data model used by these railroads when sharing data with other railroads or updating their data with Railinc/AAR to support a wide variety of collective uses.
2. GIS CAPABILITIES -- The ArcGIS Railroads Data Model needs to contain design elements that allow railroad data to fully exploit the capabilities of the ArcGIS system, to include:
a. Linear Referencing--specifically using ArcGIS Location Referencing tools.
b. Network datasets--for solving best paths, service areas, and other network analysis capabilities.
c. Trace Networks--for topological validation and all types of network tracing functions.
d. Network Diagrams--for schematic mapping, and supporting track chart creation and update.
e. Straight-Line Diagrams--for mapping point and span data of assets and phenomena.
f. Field Mobility--for asset inspection, incident reporting, maintenance of way operations, and more.
g. Real-Time mapping--of vehicles, personnel, and stationary sensors.
h. Parcel maintenance--as a base for managing real estate property, structures, and other assets.
i. Artificial Intelligence--for building and using deep learning models for automating data collection.
Please feel free to let us know below if you have any questions or comments about this. Thanks!