Climate change is one of the most pressing challenges of our time, and its impacts are being felt across various sectors, including railway operations. The need for resilience in rail infrastructure is paramount as extreme weather events become more frequent and intense. Resilience ensures that railway systems can withstand and recover from disruptions, maintaining reliable and efficient transportation services. In this article, we will explore how big data will play a crucial role in enhancing railway operations' resilience amidst climate change.
Climate change poses significant risks to rail infrastructure. Rising temperatures can cause thermal expansion of tracks, leading to buckling and misalignment. Heavy rainfall and flooding can damage tracks, bridges, and signaling systems, disrupting train services. Particularly for Canada, the melting of permafrost and the degradation of peatlands can undermine the stability of rail foundations, increasing the risk of track settlement and derailments.
Permafrost and peatlands are two climate change-related challenges that have significant implications for railway operations. Permafrost, which is perennially frozen ground, is thawing due to rising temperatures, leading to ground subsidence and instability. Peatlands, which are carbon-rich wetlands, are also experiencing degradation, causing land settlement and increased flood risk. Big data can help address these challenges by monitoring ground temperature and moisture levels, detecting early signs of thawing or degradation. With this information, railway operators can implement targeted measures such as insulation techniques or drainage systems to mitigate the impact on rail infrastructure.
On 25 May 2020, there was a derailment near Ignace, Ontario, that involved 53 train cars. Investigations led by Canada’s Transportation Safety Board (TSB) strongly suggested that soft and saturated subgrades were the cause. This lack of visibility severely hampers the ability of railway operators to direct resources towards rail sections that need them the most.
Climate change also affects the rail tracks itself and the surrounding natural environment. There was a 2019 derailment in Faust, Alberta that involved 21 tank cars. The TSB found that fluctuating temperatures had led to large compressive and tensile longitudinal forces to cause continuous welded rails (CWR) to expand rapidly, bend and misalign. These high ambient temperatures were largely unprecedented, and is believed to be a result of global warming trends. Another phenomenon is ice jacking; as ice melts, water can accumulate at the base of the tracks, leading to pooling. Over time, freeze-thaw cycles result in the formation of ice buildup, making rails susceptible to gauge spreading when a train passes. These conditions pose a heightened risk for derailments, particularly as the frequency and intensity of freeze-thaw cycles increase with the changing climate.
There is a nascent understanding of how global warming affects rail conditions over time. In order to effectively address these challenges, railway operators need accurate and timely data to inform proactive decision-making.
Big data analytics can revolutionize railway operations by providing valuable insights into the performance and condition of infrastructure assets. By collecting and analyzing vast amounts of data from various sources, such as sensors, satellites, and historical records, railway operators can gain a comprehensive understanding of their network's vulnerabilities and optimize maintenance strategies accordingly.
Specific to ApoSys, our proprietary machine learning algorithms will compare past inspection alongside real-time sensor inputs; these can predict when crucial components like tracks or rolling stock might require maintenance. Potential failures are identified timely, of which this proactive approach minimizes downtime, reduces maintenance costs, and enhances passenger safety. These algorithms also incorporate local environmental and surrounding asset data to quantitatively understand how climate change impacts the rail infrastructure. In the long-term, high-resolution climate models are developed to preemptively alert railway operators to direct resources towards safeguarding it.
All this is possible thanks to our Autonomous Track Measurement Unit (ATMU). It forms the basis of our Apollo Framework, and integrates a suite of heterogeneous sensors like LiDAR, GPR, and high-speed cameras that can achieve a 90% defect detection rate, enhancing visibility for maintenance crews. Its modular, portable design allows for easy installation under train carriages, optimizing space for sensors, ensuring that data is collected in real-time, under real-life conditions.
By providing valuable insights, improving maintenance strategies, enhancing safety, and enabling proactive decision-making, big data analytics can help railway operators adapt to the challenges posed by extreme weather events and changing environmental conditions. Our efforts to integrate innovative technologies through the ATMU will augment the ability to collect data and analyze it. The future of resilient railway operations lies in embracing big data and leveraging its power to build a sustainable and efficient rail network. Let's go full steam ahead to transform the railway sector – contact us today to explore the ApoSys advantage.