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DEA-neural networks approach to assess the performance of public transport sector of India

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  • Shivi Agarwal

    (BITS)

Abstract

This paper proposes the integrated Data Envelopment Analysis-Neural Networks approach to measures the efficiency of public transport sector of India. Data have been collected for 30 State Road Transport Undertakings (STUs) for the year 2011–2012. Efficiency of the STUs is measured with the use of three inputs and single output. Fleet Size, Total Staff and Fuel Consumption are considered as inputs and Passenger Kilometers as output. On the basis of the status of efficiency, it is concluded that efficiency of the STUs are not good and very far from the optimal level. In order to check the robustness of the results, regression and correlation analysis are also conducted which reveal that the efficiency scores measured by all the models having the common trends. The most efficient and the lowest efficient STUs are found same by all the models. The results also demonstrate that the proposed models are highly flexible and don’t require any prior assumptions about the functional form between inputs and outputs. The models also handle the problem of the presence of the outliers and statistical noise in the data points.

Suggested Citation

  • Shivi Agarwal, 2016. "DEA-neural networks approach to assess the performance of public transport sector of India," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 248-258, June.
    • Handle: RePEc:spr:opsear:v:53:y:2016:i:2:d:10.1007_s12597-015-0229-2
    DOI: 10.1007/s12597-015-0229-2
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    References listed on IDEAS

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    1. Shivi Agarwal & Shiv Prasad Yadav & S.P. Singh, 2011. "A new slack DEA model to estimate the impact of slacks on the efficiencies," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 12(3), pages 241-256.
    2. Francisco J. Delgado, 2005. "Measuring efficiency with neural networks. An application to the public sector," Economics Bulletin, AccessEcon, vol. 3(15), pages 1-10.
    3. Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    6. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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    Cited by:

    1. Fei Ma & Xiaodan Li & Qipeng Sun & Fei Liu & Wenlin Wang & Libiao Bai, 2018. "Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China," Sustainability, MDPI, vol. 10(7), pages 1-23, July.
    2. Shiva Moslemi & Abolfazl Mirzazadeh & Gerhard-Wilhelm Weber & Mohammad Ali Sobhanallahi, 2022. "Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1116-1157, September.
    3. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1-24, January.

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