How Metro and Rail Use Data to Optimize Business

rail transportationIoT and Big Data in rail transportation

It’s a little known fact that rail transportation is one of the earliest adopters of IoT and Big Data. Already sensors and devices along rail tracks are helping keep rail systems safe and secure. Increasingly, operators are also using data generated from train station turnstiles, entrances and temperature sensors, for example, to optimize business. Below are some of the ways data is helping retain customers and generate more revenue.

Data is being used for predictive maintenance. This helps staff identify maintenance issues before they impact operations, revenue or safety.

In addition, rail operators use data to reduce rail congestion. For example, data on peak hour ridership, areas with the most traffic, and data collected from entrances, exits, and speed gates can help operators optimize train schedules.

Thirdly, with analytics such as queue management and direction flows combined with video analytics, operators can also make decisions on where to deploy staff during peak times in order to ensure better customer service.

Know more about customers

Ultimately, Big Data from rail sensors can help operators learn more about their customers. This way they can come up with focused and targeted marketing strategies to optimize revenue and deliver a more personalized customer experience. “The opportunity exists for station operators to adopt cognitive computing capabilities to re-route trains, inform passengers of their choices and alternatives, and provide real-time updates through text, social media and other channels,” said Keith W. Dierkx, Global Industry Leader for Rail at IBM. “Our studies have shown us that timely and correct information combined with providing alternatives drives much higher traveler satisfaction.”

And operators can now take data-driven personalization efforts a step further. “It is now possible for a passenger to share his or her calendar with the system, so it knows the passenger’s departure time and what meetings are planned at the end of the journey. By interpreting this data, the cognitive system knows how to interact with the passenger in real-time to make a restaurant suggestion, offer an earlier train or an alternate route and propose a quiet place to hold the meeting once the passenger has arrived at his or her destination,” Dierkx said.

Read the whole article on asmag.com.

For more information about IBM travel and transportation industry expertise, please visit www.ibm.com/industries/traveltransportation/

Industry Marketing Manager - Travel & Transportation

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