Now we are cognizant of tightening budgets and increasingly more reviews of public expenditures, so there is a need for an objective analysis of the performance of public bodies in terms of efficient execution of their tasks. Questions occur throughout the public domain, for instance, in the provision of medical facilities, the operation of postal services, and especially the supply of public transportation. The management environment surrounding public transportation is growing increasingly severe in recent years. Public transportation authorities implement strategies to improve management efficiency. Data Envelopment Analysis (DEA) has become an established approach in the analysis of efficiency problems for both public and private sectors. In the past few years, much progress has been made to extend this approach in various directions. This study newly proposed a NCN-CD model in DEA that integrates a non-controllable variable (NCN) model and a context-dependent (CD) model. The NCN model can analyze an efficiency problem when some of the inputs or outputs are exogenously fixed and beyond the discretionary control of Decision Making Units (DMU) managers. The CD model refers to a DEA approach in which a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by the DMUs at a specific performance level. Each efficient frontier contains a possible target for a specific DMU to improve its performance. The CD model yields efficient frontiers at different levels, while being based on a level-by-level improvement projection. In this study, we set DMUs for 9 city transportation authorities and 16 major private railway companies in Japan. Input items were operating costs, and total asset of the DMUs. Output item were the operating revenues of the DMUs. The non-controllable variable was set as the total assets. The above-mentioned NCN-CD model was applied to the data with the aim to measure and improve the efficiency of management in these authorities and companies based on the inputs and outputs. JEL Classification: R40, C44