EVALUATING PUBLIC TRANSPORT EFFICIENCY WITH NEURAL NETWORK MODELS
ridership - commuting, mode - mass transit
Transit, Public transit, Mass transit, London Underground, London Transport, Local transit, Least squares method, Efficiency, Economic efficiency, Econometric 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.
Costa, A, Markellos, R, (1997). EVALUATING PUBLIC TRANSPORT EFFICIENCY WITH NEURAL NETWORK MODELS. Transportation Research Part C: Emerging Technologies, Volume 5, Issue 5, p. 301-312.