Making railway the mobility choice of tomorrow is a big challenge. At KONUX, we started our journey with KONUX Switch – the predictive maintenance solution for railway switches and crossings (prediction accuracy now verified by DB). Now KONUX is looking beyond the switch and is expanding its portfolio to bridge the gap between infrastructure maintenance and operations to bring more capacity, reliability, and cost efficiency to railways.
Many key network-wide decisions such as inspection cycles (regimes), remediation timescales or resource allocation are based on how much traffic runs over the infrastructure or a track section. However, today’s information about tonnage, speed, and traffic is often unreliable and outdated. Planned and actual traffic can differ by as much as 40%. This leads to over- and under-inspection, loss of operational capacity, blind spots for infrastructure degradation, and ultimately poor resource allocation.
KONUX Network leverages AI and large-scale fusion from our deployed IIoT devices, customer data and 3rd party data. It generates a model of actual daily traffic and its impact throughout the network. The insights into actual network usage help national and regional managers, route directors, and planners to optimise inspection planning, allocate resources where they are needed, and tackle capacity bottlenecks.
The Network Rail model comprises 35,500 track sections and 480,000 individual monthly train journeys. The usage monitoring allows users not only to see passenger traffic, but notoriously unpredictable freight traffic as well. The resulting model of actual daily traffic and load helps visualise how tonnage and speed propagate throughout the network.
The identified mismatch between planned and optimal track categories defining inspection cycles is striking. Re-assigning a Network Rail track from track category 2 (high expected traffic / tonnnage) to track category 3 (actual lower traffic/tonnage) may result in saving 2 hours of inspections per mile per month!
Shifting the perspective from topology to geography allows regional managers and route directors to identify which routes/areas/regions have savings potential and which require more regular inspection. Lastly, this allows them to lead a data-driven dialog for better resource allocation amongst peers and within managed teams.
Overall, KONUX Network provides deep insight into infrastructure usage – saving time on track and re-allocating resources where they are needed most to enable more operational capacity. And yet KONUX’s journey does not end there…
Rail traffic volume is rising and is expected to double by 2050. This is already leading to key city connections operating above planned capacity and below targeted punctuality. KONUX Traffic leverages AI and data fusion to build a model of capacity used and delay spread based on actual traffic patterns over time. This helps time table planners identify untapped capacity reserves, proactively improve delay robustness, and better plan maintenance track access windows – resulting in higher operational capacity and punctuality.
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