Application of the LP-ELM Model on Transportation System Lifetime Optimization

Considering factors such as economic costs and lives, an unreliable transportation system is more likely to cause severe consequences. Therefore, reliability optimization of transportation systems has attracted much attention over the past several decades. The traditional reliability optimization design is usually focused on redundancy allocation or reliability redundancy allocation. In practice, the operation process usually has a significant influence on the transportation system lifetime. By combining linear programming (LP) and extreme learning machine (ELM), a two-stage approach is proposed to optimize the transportation system lifetime, in which a semi-Markov model (SMM) is used to model the operation process. In the proposed method, we first formulate the optimization problem as an LP model, and the LP algorithm is utilized to search for the approximate optimal state probabilities. After data production and sample selection, ELM is trained with the produced training data and used to predict the optimal sojourn time distribution parameters. Applications on three different cases demonstrate that a higher lifetime can be ensured for the transportation system by using the proposed method.

• Record URL:
  https://doi.org/10.1109/TITS.2011.2160053
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Soszynska-Budny, J
  • Sun, Zhan-Li
  • Habibullah, Mohamed Salahuddin
  • Ng, Kein Ming
  • Soszynska-Budny, Joanna
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 1484-1494
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 12
  • Issue Number: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Linear programming; Machine learning; Markov processes; Mathematical models; Neural networks; Optimization
• Subject Areas: Data and Information Technology; Highways; I71: Traffic Theory;

Filing Info

• Accession Number: 01359044
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: TLIB
• Created Date: Dec 16 2011 2:47PM