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article imageSiemens, data analytics and train efficiency

By Tim Sandle     Nov 1, 2017 in Business
Transport systems are becoming increasingly integrated and customers are expecting improved efficiency. To help train operating companies with this task, Siemens have shown how big data analytics can be deployed.
Transport integration is a general term covering all modes or types of transport (such as rail, road, water, 'niche', air), and bringing these together so that they all operate as one 'seamless' entity. This is for the benefit of the fare paying customer.
This article follows on from an overview of the benefits of integrated transport, titled “Modern economies need an integrated transport structure.”
Achieving effective transport integration is not straightforward since one delay in a service can cause the entire system to buckle, pushing the delays from one service to another. One area notorious for delays is with trains. Trains can become delayed due to signaling issues, spending too long at a platform, or with having to run more slowly due to inclement weather.
To help to improve train operations, the German company Siemens has been harnessing the computational power of big data analytics to make steadfast improvements to train operating schedules.
This is being tackled in a number of ways, according to Computer World. In one example, Siemens has being making use of sensors designed to analyze information about trains and tracks. This data review has enabled train operators to shift their maintenance models from being simply reactive to becoming proactive.
Seibu railway s smile train
Seibu railway's smile train
Another example is through the assessment of the condition of components, undertaken through diagnostic sensor data. This allows operators to detect and understand patterns that can predict when a failure is likely to arise.
Moreover, to avoid disruptions to service, equipment from Siemens allows operators to monitor information in near real-time. This leads to the disruption of services being minimized. As an example, where component anomalies are detected, the component can be replaced while the potential faulty part can be sent for inspection.
This review is becoming increasingly sophisticated and Siemens has worked with scientists and engineers in order to develop algorithms for the prediction of failures of train components and railway infrastructure.
These measures appear to be successful, according to The Financial Times. In a joint venture with Renfe, the Spanish rail network, new statistics shows only one-in-2,300 Siemens-hauled journeys was more than five minutes late. This gives a punctuality rate of 99.98 percent.
For greater reliability the company is also developing the Internet of Trains, as Gerhard Kreß, director of mobility data services at Siemens, told Forbes recently: “Customers have invested a lot into their assets – trains, rails, signaling – and our task is to help them get better returns.”
Big data can also be used to develop apps attuned to the integrated transport paradigm. One example is currently being piloted. To read more about this see the article: “New integrated transport app tested.”
More about Trains, mobility as a service, big data analytics, Transport, integrated transport
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