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![]() Least Squares (PLS) regression was used to determine the most influential input Variables accordingly with regression model where it was relevant. Long route problem by changing the most important input variable and output Performance, scenarios were created for relatively underperforming routes and ![]() To explore the possibility of enhancing the To access the relative performance of transit system in absence of historicalĭata and research to compare with. (DEA) in terms of efficiency and effectiveness score. Public Transit System, Data Envelopment Analysis, Performance Evalua-tion, Partial Least Squares RegressionĪBSTRACT: This study evaluates the operational performance of all routes of Sajhaīus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis Public Transit Performance Evaluation Using Data Envelopment Analysis and Possibilities of Enhancement (2003) Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets And DEA Excel Solver.
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