Evaluating public transport efficiency with neural network models

Costa, Álvaro; Markellos, Raphael N.

doi:10.1016/s0968-090x(97)00017-x

Cited by 95 publications

(55 citation statements)

References 6 publications

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“…See further Ref. 10. Also, the proposed ANN approaches can learn from experience and can generalize, estimate, predict, with few assumptions about data and relationships between variables.…”

Section: Discussionmentioning

confidence: 99%

Global Approximations to Cost and Production Functions using Artificial Neural Networks

Tsionas¹,

Michaelides²,

Vouldis³

2009

IJCIS

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The estimation of cost and production functions in economics usually relies on standard specifications which are less that satisfactory in numerous situations. However, instead of fitting the data with a pre-specified model, Artificial Neural Networks (ANNs) let the data itself serve as evidence to support the model's estimation of the underlying process. In this context, the proposed approach combines the strengths of economics, statistics and machine learning research and the paper proposes a global approximation to arbitrary cost and production functions, respectively, given by ANNs. Suggestions on implementation are proposed. All relevant measures such as Returns to Scale (RTS) and Total Factor Productivity (TFP) may be computed routinely.

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Section: Discussionmentioning

confidence: 99%

Global Approximations to Cost and Production Functions using Artificial Neural Networks

Tsionas¹,

Michaelides²,

Vouldis³

2009

IJCIS

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“…The results are shown in Table 3 through Table 5. In Table 3 the rankings of the power plants based on Athanassopoulos and Curram (1996) study which is called ''standardized efficiency'' is shown (Costa & Markellos, 1997;Delgado, 2005;Azadeh et al, 2007). Also Table 4 shows the calculation results according to Azadeh et al (2007) approach.…”

Section: Validity Verificationmentioning

confidence: 99%

A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Ghomi¹,

Ansarinejad²,

Razaghi³

et al. 2014

MAS

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This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn't need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

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“…In empirics, Costa and Markellos (1997) found the so-called 'congested area' in the London underground from 1970 to 1994 in terms of fleet size and workers (inputs) and millions of trains km per year covered by fleet (outputs). Some researchers in educational production function analysis found that traditional restrictive specifications fail to capture potential non-linear effects of school resources (Baker, 2001).…”

Section: Experiments Designedmentioning

confidence: 99%

“…In some cases, this approach has also been explored by Athanassopoulos and Curram (1996), Costa and Markellos (1997), Wang (2003), Santin et al (2004) and Delgade (2005) in handling efficiency measurement problem, but rarely in an international framework with whole countries/industry sectors as units of observation. The majority of the above references has reported that efficiency scores derived from neural networks, if not better than those from traditional approach, at least provide an additional/alternative direction to this problem.…”

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confidence: 99%

Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies’ Growth Decomposition

Liao

Wang

Weyman‐Jones

2007

Global Economic Review

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There has been a long tradition in business and economics to use frontier analysis to assess a production unit's performance. The first attempt utilized the data envelopment analysis (DEA) which is based on a piecewise linear and mathematical programming approach, whilst the other employed the parametric approach to estimate the stochastic frontier functions. Both approaches have their advantages as well as limitations. This paper sets out to use an alternative approach, i.e. artificial neural networks (ANNs) for measuring efficiency and productivity growth for seven East Asian economies at manufacturing level, for the period 1963 to 1998, and the relevant comparisons are carried out between DEA and ANN, and stochastic frontier analysis (SFA) and ANN in order to test the ANNs' ability to assess the performance of production units. The results suggest that ANNs are a promising alternative to traditional approaches, to approximate production functions more accurately and measure efficiency and productivity under nonlinear contexts, with minimum assumptions.

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Evaluating public transport efficiency with neural network models

Cited by 95 publications

References 6 publications

Global Approximations to Cost and Production Functions using Artificial Neural Networks

Global Approximations to Cost and Production Functions using Artificial Neural Networks

A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies’ Growth Decomposition

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