Increased Track Capacity through Smart Algorithms

Increased Track Capacity through Smart Algorithms

Since this year, OXplus has joined RailCargo as a participant. What does a company that specialises in Enterprise Asset Management have to do among rail operators, terminals and other logistics service providers? Richard van Dongen, Chief Commercial Officer and co-founder, explains what they do to optimise track capacity.

The company from Veghel, that won a bronze Gazellen Award for the fastest growing companies in the Netherlands in 2020, was founded in 2010 and focuses on (preventive) maintenance and optimisation of the deployability of equipment in the transportation industry. How can you use software and the analysis of big data ‘assets’ such as wagons, locomotives, metro carriages and railways to keep them optimally deployable? “We are also active in capacity planning,” adds Van Dongen. “In the case of the rail industry it’s all about making the best possible use of the track. You can use algorithms to achieve optimum planning, but you can also advise in real time in the event of disruptions”.

Fewer transactions on the tracks

A good example of this is the Capacity Planner, that was developed together with NS. Using smart algorithms, the Capacity Planner provides an automated (autonomous), data-driven planning process that replaces a rather inefficient way of maintenance planning with many manual actions. “This leads to better and more efficient maintenance of trains, less downtime and fewer unnecessary kilometres due to re-routing. The Capacity Planner is the tool to make First-Time-Right decisions in the planning and maintenance process by showing the right data to the right decision makers at the right time. In this way, the Capacity Planner has increased maintenance capacity by more than thirty percent for NS. This also leads to fewer transactions on the tracks”.

Van Dongen believes that this type of digitisation solutions can add value to rail freight traffic in various ways. “Of course by planning maintenance much better and switching more to preventive actions and even automating these with a self-learning system, based on smart algorithms. So far, we have been doing this mainly in the passenger transportation industry, but for a major German client we are supporting the planning of around 600 locomotives for freight traffic. The principles are the same.”

Optimal stabling capacity and track utilization

Van Dongen points out another application by his company for optimizing, for example, the stabling capacity: “The problem we solved with the Capacity Planner at the time was that more than 100 new trainsets were added and these had to be maintained with the same (maintenance) capacity. As a result, there was a need for a smart system, that would ensure that the maintenance and service capacity was used optimally and increasingly smarter. The same algorithms we used for Capacity Planner can also ensure optimisation of stabling capacity. In this way, the distribution of the tracks for freight and passenger transportation can be regulated in a more transparent and smarter way. In this way, you get the most out of the existing track with a system that continues to learn and always makes the best choices. This can help new parties who want to connect to the railways.”