Historically, water events have been one of the biggest causes of concern for the railway industry. High waters from flash floods and heavy rain wreak havoc with infrastructure. Roadbeds can become soft and saturated while track and switching equipment is undermined. The result is frequent delays at best and derailments, spills, and costly repairs at worse.
Despite the increased availability and accuracy of weather forecasting technology, rail operators and transportation companies still suffer regular delays due to water events. But if becoming better forecasters isn’t helping, is there anything else companies can do?
Upgrading technology and taking steps to incorporate the industrial IoT would go a long way to helping rail and transportation companies reduce water event disruption. Here are three tips to make that happen.
1. Improve Analytics
Weather forecasting technology is already making great strides in predicting issues in advance. But more can be done. By incorporating industry and location-specific data into analytics, such as ground elevation, past events, and previous outcomes of water events, rail companies can leverage this data to predict the location and impact of water events more accurately.
When large, unavoidable events do occur, companies will be able to ration resources appropriately, giving more support to locations and parts of the infrastructure system that have caused issues in the past.
2. Invest in Machine Learning
Analytics only go so far. Not only is it impossible for humans to handle the large quantity of data that’s becoming available, but humans also introduce more room error. After pouring over numbers for hours, it’s much more likely that they’ll miss something.
That’s why incorporating machine learning is the next obvious steps for rail operators. Updating software and systems, and introducing machine learning and IoT technology will allow operators to comb through large quantities of data effectively and automate water event prediction. When issues are raised, instant alerts can be sent out to key personnel, allowing staff to analyze act on the advice of the AI. In this scenario, machine learning does the grunt work of data processing, while humans do the more strategic work of reviewing alerts and determining the best way to respond.
3. Introduce Sensors to Tracks as well as Trains
Several train operators around the world are introducing real-time sensors into their carriages, which helps them get more data that machine learning systems can use to make accurate predictions. Unfortunately, few are incorporating sensors into tracks and other key infrastructure, creating holes in their data.
Operators that want to reduce water event disruption should take a more holistic approach to data collection. By incorporating real-time sensors into key infrastructures like tracks, bridges and switching points, engineers can have a network-wide view of maintenance issues when water events do occur. Rather than having to wait until failures occur to fix issues, staff can proactively assess and repair those parts of the system that show they are most at risk of failing.
Use IoT, Machine Learning, and Analytics Can Help Rail and Transportation Companies Reduce Disruption from Water Events
Incorporating the IoT into the rail industry isn’t a cost, it is an investment. One that several national companies are already making. By being able to better predict weather issues and maintenance problems in advance, and have real-time data to back that up, transportation companies can significantly reduce delays and the frequency and cost of repairs. Water event disruptions won’t go away completely, but they’ll soon become less of an issue.