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

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.