NS Spending Big on Technology Development

Norfolk Southern budgeted nearly $300 million this year for technology development, including $140 million for positive train control.

Railroad management said that efforts to develop more technologies and automation have accelerated over the past few years and have included such things as predictive analytics to machine learning to artificial intelligence to smart sensors.

The budget for technological developments this year has sharply risen from $77 million in 2016 to $110 million in 2017.

PTC is viewed by NS executives as a major step toward the goal of an automated railroad.

NS expected to have PTC fully in place by the end of 2020 and is expected to seek an implementation extension later this year from the Federal Railroad Administration.

Along the way NS hopes to be able to persuade government regulators to change their regulations to account for the development of technology.

One example given by NS in that regard involves switch inspection. The railroad has sensors that monitor switch, but the FRA still requires visual inspections of them twice a month.

What NS hopes to see is what it described as performance-based regulations that would allow the railroad industry to experiment and innovate

At the same time, NS acknowledges a role for federal regulators to establish safety parameters.

NS sent the FRA a 41-page document to the FRA that defines how regulators could partner with the railroad industry to promote innovation.

Among the technological systems that NS is seeking to develop include predictive models that forecast when track curves will need replacement.

That model uses machine learning, artificial intelligence and data points collected from on-track equipment to create algorithms that can predict track wear over a five-year period.

This will enable managers can better plan repairs and maintenance, and reduce track downtime.

Although railroads use geometry cars to inspect rails, that is done after the fact and doesn’t provide information that predicts when rails need to be repaired or replaced.


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