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How Swiss Federal Railways SBB is Transforming Train Operations With MongoDB

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By Dr. Humza Akhtar and Iman Moheb

The railway sector is witnessing a rapid adoption of digital technologies. Digitalization in rail operations has the potential to improve the efficiency and productivity of station staff, drivers, operations and management staff by unlocking access to real-time data. National railway company Swiss Federal Railways SBB has always been at the forefront of innovation. From creating a condition-based train cleaning system to obtaining real-time updates of where vehicles are on the track, SBB is revolutionizing the way railway operations are conducted. These solutions are setting new standards for efficiency, reliability, and passenger experience. In this blog, we look at two such applications that are powered by MongoDB Atlas.

Cleaning 4.0 App

In 2022, SBB spent billions cleaning and maintaining the trains. For a number of reasons, cleaning trains is highly inefficient. Train configurations are not uniform, and train schedules are tight, leaving little time for cleaning and maintenance between trips. The amount of work it takes to clean trains varies based on passenger traffic. In addition, hard-to-reach areas, weather factors, and the need for tight quality control add further complications. Effective communication between cleaning crews is also important to make sure that the cleaning operation runs smoothly. Oftentimes, cleaning crews would post their schedules up to a month before the actual job, which was inefficient. All these challenges required a data-driven solution that could calculate the exact cleaning requirements of each train as it comes into the station and then synchronize these requirements with the cleaning crews, all while minimizing disruptions to schedules.

SBB started a project called Cleaning 4.0. Instead of adhering to fixed, pre-planned schedules, cleaning can take place based on actual requirements. The team needed to create a solution that provides accurate information of train status to the cleaning crews. To determine optimal cleaning times and tasks, the solution employs an analytical model that works regardless of which train route was being looked at. The process starts by obtaining the train timetable and gauges cleaning requirements by correlating the distance traveled since the last cleaning for each train, passenger traffic, and other related parameters.

As the train moves, it generates telemetry data for location, distance traveled, and traffic. This data is captured by MongoDB Atlas and serves as inputs to the analytical model. The output of the model is actually the cleaning task list, which is then synchronized with a mobile application that cleaners use to check their daily tasks. This mobile app leverages Atlas for the Edge to easily manage data. Using Atlas Device Sync, it persists this incoming data and syncs it to the cloud. This application facilitates the cleaning operation and increases efficiency. The cleaning app is planned to go live in 2024.

Leveraging Atlas Device Sync, it becomes very easy to build and maintain new applications because of bi-directional sync protocol, data compression in flight, and resiliency to network interruptions. According to the SBB team, they can save months of development time because of MongoDB because developers do not have to worry about building APIs for connectivity to a cloud backend or dealing with complex edge cases and conflict resolution. It is easier to maintain mobile applications using Atas Device Sync and thus the company can spend more time on driving innovation.

Gleisspiegel (Track mirror app)

Knowing where train engines and carriages are parked within the yard is critical to efficient operations. It enables the company to deploy maintenance and cleaning crews efficiently, ensures timely inspections, and optimized engine rotations, ultimately minimizing downtime. Keeping a real-time record of parked train engines is not an easy task because of frequent movement of engines for maintenance, repairs, and rearrangements. Train yards are large, and the staff was using Excel sheets to keep track of engines, leading to potential errors and data conflicts.

The solution was a track mirror app (Gleisspiegel) that continuously monitors the location of all engines and cars parked in the yard. With the mobile app in the hands of engine operators, whenever a new engine is parked at a space or moved between spaces, the operator updates the location in the mobile app. The mobile app persists the data using Atlas Device Sync and then the location information is updated on all the other mobile apps through Atlas Device Sync and to the web app through method. This harmonious synchronization is facilitated by leveraging conflict resolution from Atlas Device Sync. MongoDB Atlas acts as the cloud backend, consolidating location data from all the engines and cars.

The precise, real-time location tracking enhances accessibility to train assets and increases efficiency of daily operations. Both iOS and Android versions have been developed leveraging the Flutter SDK. Looking ahead, the team at SBB plans to add analytical capabilities to the app to optimize parking by looking at trends over time.

To learn more about MongoDB’s role in the mobility industry, please visit our Automotive and Manufacturing page.

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