EVALUATING PUBLIC TRANSPORT EFFICIENCY WITH NEURAL NETWORK MODELS

This paper is concerned with measuring performance of public transport services based on the concept of productive efficiency. A new nonparametric approach is proposed based on multi-layer perception neural networks (MLPs). The advantages and limitations of this approach are discussed and compared with those of mathematical programming and econometric techniques. The MLP is used, along with data envelopment analysis (DEA) and corrected least squares (COLS), to set out comparative annual efficiency measures for the London Underground, for the period 1970 to 1994. It is argued that the MLP approach is superior to traditionally applied techniques since it is both nonparametric and stochastic and offers greater flexibility. Finally, it is demonstrated that the proposed MLP efficiency analysis has important practical implications for decision making.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Corporate Authors:

  Elsevier

  The Boulevard, Langford Lane
  Kidlington, Oxford  United Kingdom  OX5 1GB
• Authors:
  • Costa, A
  • Markellos, R N
• Publication Date: 1997-10

Language

• English

Media Info

• Features: References; Tables;
• Pagination: p. 301-312
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 5
  • Issue Number: 5
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Econometric models; Economic efficiency; Least squares method; Public transit
• Identifier Terms: London Transport; London Underground
• Uncontrolled Terms: Efficiency
• Subject Areas: Economics; Highways; Operations and Traffic Management; Public Transportation; I71: Traffic Theory;

Filing Info

• Accession Number: 00744998
• Record Type: Publication
• Report/Paper Numbers: SP-168
• Files: TRIS, ATRI
• Created Date: Jan 26 1998 12:00AM