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Cited by 25Railway Systems and Energy EfficiencyAerodynamics and Fluid Dynamics Researchtransportation and logistics systems

Machine vision analysis of the energy efficiency of intermodal freight trains

Lai, Y-C, Barkan, C P L, Drapa, J, Ahuja, N, Hart, J M, Narayanan, P J, Jawahar, C V, Kumar, A +2 more·2007·Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
DOI 10.1243/09544097jrrt92
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Abstract

Intermodal (IM) trains are typically the fastest freight trains operated in North America. The aerodynamic characteristics of many of these trains are often relatively poor resulting in high fuel consumption. However, considerable variation in fuel efficiency is possible depending on how the loads are placed on railcars in the train. Consequently, substantial potential fuel savings are possible if more attention is paid to the loading configuration of trains. A wayside machine vision (MV) system was developed to automatically scan passing IM trains and assess their aerodynamic efficiency. MV algorithms are used to analyse these images, detect and measure gaps between loads. In order to make use of the data, a scoring system was developed based on two attributes - the aerodynamic coefficient and slot efficiency. The aerodynamic coefficient is calculated using the Aerodynamic Subroutine of the train energy model. Slot efficiency represents the difference between the actual and ideal loading configuration given the particular set of railcars in the train. This system can provide IM terminal managers feedback on loading performance for trains and be integrated into the software support systems used for loading assignment.

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Crossref · 10.1243/09544097jrrt92

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  • Lai et al. use computer vision to detect aerodynamically inefficient loading on freight trains.

    “Lai et al. [461] use computer vision to detect aerodynamically inefficient loading on freight trains.”

    From Tackling climate change with machine learning (2023) · cites this paper as [461]

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Authors
Lai, Y.-C., Barkan, C. P. L., Drapa, J., Ahuja, N., Hart, J. M., Narayanan, P. J., Jawahar, C. V., Kumar, A., Milhon, L. R., & Stehly, M. P.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • Centre for Visual Information Technology, International Institute of Information Technology, Gachibowli, Hyderbad, India
  • Technical Research and Development, BNSF Railway, Topeka, Kansas, USA
  • Environment and Research Development, BNSF Railway, Fort Worth, Texas, USA
ORCID
Venue
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Publisher
SAGE Publications
Published
2007-05-01
Volume
221
Issue
3
Pages
353-364
ISSN
  • 0954-4097 (ISSN-L)
  • 2041-3017
DOI
10.1243/09544097jrrt92
OpenAlex
W2145671984
Cited by
25
References
12
Citations by year
2026: 1 · 2022: 2 · 2019: 2 · 2018: 2 · 2016: 2 · 2014: 1 · 2013: 2 · 2012: 1
FWCI
7.45
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit (publishedVersion)
Funders
Bulgarian National Science Fund
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Railway Systems and Energy Efficiency
  • Physical Sciences › Engineering › Aerospace Engineering › Aerodynamics and Fluid Dynamics Research
  • Social Sciences › Transportation › transportation and logistics systems
Keywords
machine vision, train energy efficiency, aerodynamic drag, load configuration, aerodynamic coefficients, fuel consumption

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