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 perceptron 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.