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Ambient intelligence for freight railroads

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2 Author(s)
Reason, J.M. ; IBM Research Division, Thomas J. Watson Research Center, 19 Skyline Drive, Hawthorne, New York 10532, USA ; Crepaldi, R.

Within the freight railroad industry, there is currently an industry-wide effort to enable intelligent telemetry for freight trains. The conventional wisdom is that greater visibility of the status and proper functioning of trains would enable business transformation in numerous areas, including predictive maintenance, schedule optimization, and asset utilization. However, this effort to derive business value through new technology may also have significant environmental benefits. For example, failure prediction can drastically reduce derailments, thereby protecting the environment from the harmful effects of hazardous material spills. Equally important, better visibility of trains can lead to more efficient scheduling of trains, allowing more trains to move freight. Studies show that trains are three times more fuel efficient than trucks, and transferring just 1% of truck freight to railroads can reduce greenhouse gas emissions by 1.2 million tons annually. In this paper, we describe the potential benefits of employing a new, intelligent telemetry infrastructure for freight railroads. One proposed approach called SEAIT, or Sensor Enabled Ambient Intelligent Telemetry, is a wireless sensor network approach to supporting sensing and communication for advanced freight transportation scenarios. As part of a proof-of-technology exploration, we describe some preliminary performance results of SEAIT.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:53 ,  Issue: 3 )