Efficiency measurement of railway passenger stations through network data envelopment analysis
Document Type
Journal Article
Publication Date
2023
Subject Area
place - asia, mode - rail, planning - methods, infrastructure - station
Keywords
Railway, Passenger station, Efficiency measurement, Network data envelopment analysis, Slacks-based measure
Abstract
Railways, as one of the effective and essential transportation systems, are a prerequisite to traditional economic development. The railway station is an important part of the railway transportation system. Given the scope of its functions, including passenger transportation, train route management, and commercial facilities, evaluating their performance to optimal allocation of resources and providing technical infrastructure is particularly significant. Also, aviation is a prerequisite to modern economic development. So, according to the benchmark of railway and aviation industries efficiency-related research, this study evaluates the efficiency of passenger stations in 19 Iranian railway regions by network data envelopment analysis (NDEA). Given that the efficiency of subprocesses in a system is vital in evaluating its overall efficiency, the performance of railway stations is evaluated using an output-oriented slack-based measure (SBM) model with variable return-to-scale (VRS), determining the efficient stations and reference units for inefficient ones. Ultimately, projected values were presented for inefficient stations to improve their performance. Comparing the proposed approach with traditional models revealed its superior discriminating power to the black-box and separation models, enabling it to offer a more profound insight for improving station management.
Rights
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Recommended Citation
Foroutan, S.L.F., & Bamdad, S. (2023). Efficiency measurement of railway passenger stations through network data envelopment analysis. Research in Transportation Business & Management, Vol. 46, 100767.
Comments
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