Data envelopment analysis: an application in the transport sector

2006 ◽  
Vol 9 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Punita Saxena ◽  
K.K. Dewan ◽  
M. Mustafa
Keyword(s):  
Data Envelopment Analysis ◽  
Transport Sector ◽  
Data Envelopment

ASSESSING THE EFFICIENCY OF LITHUANIAN TRANSPORT SECTOR BY APPLYING THE METHODS OF MULTIMOORA AND DATA ENVELOPMENT ANALYSIS

Transport ◽  
2011 ◽  
Vol 26 (3) ◽  
pp. 263-270 ◽  
Author(s):  
Alvydas Baležentis ◽  
Tomas Baležentis
Keyword(s):  
Data Envelopment Analysis ◽  
Data Availability ◽  
Production Factor ◽  
Transport Sector ◽  
Scale Efficiency ◽  
Economic Downturn ◽  
Data Envelopment ◽  
Multi Objective Optimization ◽  
Limited Data ◽  
Full Capacity

This study focuses on evaluating Lithuanian transport sector throughout 1995–2009 by applying multi–criteria decision making method MULTIMOORA (Multi–Objective Optimization plus the Full Multiplicative Form) and data envelopment analysis (DEA). MULTIMOORA provided ranks that enabled to perform time series analysis, whereas DEA made possible to identify both technical and scale inefficiencies. Due to limited data availability, we analyzed the transport sector as a whole, i. e. it was not decomposed into that of land, air, railway or water. Although every production factor, including labour, capital and land is required for developing the transport sector, due to limited data availability, it is not possible to tackle them all when performing analysis. Consequently, one input, namely energy consumption in transport, was considered in the conducted analysis. On the other hand, two forms of transport – passenger and freight transport – were distinguished, and each of them was measured using composite indicators of passenger and tonne kilometres respectively. These two indices were considered as the outputs of transport sector activity. The final ranks provided by MULTIMOORA indicate that the transport sector was operating most effectively during 2004–2008, whereas it exhibited relative inefficiency throughout 1996–1998. The application of DEA suggests that the efficiency downturn of 1996–1998 might have been caused by technical inefficiency, whereas that of 2008–2009 was driven by scale inefficiency. Indeed, the technical modernization of the transport sector as well as the resolution of resource allocation problems might have lead to an increase in technical efficiency. Meanwhile, economic downturn prevents the transport system from working at full capacity; hence, scale efficiency is still observed.

scholarly journals Efficiency Evaluation of State Transport Undertakings of India using DEA-NN approach

2015 ◽  
Vol 10 (5) ◽  
pp. 2189-2198
Author(s):  
Dr. Punita Saxena ◽  
Dr. Amita Kapoor
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Envelopment Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Transport Sector ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Data Set ◽  
Policy Makers

The economy of any nation depends on the structure and functioning of its various sectors. Transport sector is one of the vital sectors for the financial system of any developing country. All other sectors are dependent on it either directly or indirectly. Thus improving the efficiency of this sector has become a major concern for the operators and the policy makers. The present paper presents an amalgamation of the two non-parametric techniques, Data Envelopment Analysis (DEA) and Neural Networks (NNs) to compute the efficiency scores of State Transport Undertakings of India. DEA is used to compute the efficiency scores of 27 DMUs. These scores are used to train a neural network model, namely the BPN model. The algorithm is developed and used for predicting the efficiency scores of other units of the data set. The results obtained are comparable and it has been shown that this approach helps in improving the discriminatory power of DEA.

scholarly journals A Benchmarking Strategy for Delhi Transport Corporation: An Application of Data Envelopment Analysis

2019 ◽  
Vol 4 (1) ◽  
pp. 232-244 ◽  
Author(s):  
Punita Saxena
Keyword(s):  
Regression Analysis ◽  
Data Envelopment Analysis ◽  
Urban Areas ◽  
Returns To Scale ◽  
Road Transport ◽  
Transport Sector ◽  
Data Envelopment ◽  
Data Set ◽  
Explanatory Variables ◽  
Input And Output

The growth of any developing economy depends largely on its transport sector. Growing economy leads to more job opportunities and movement of people from rural to urban areas. Public road transport hence plays a significant role as a support system in carrying passengers. This paper discusses the efficiency of State Transport Undertakings of India, in particular, Delhi Transport Corporation (DTC) using the technique of Data Envelopment Analysis (DEA) and regression analysis. A data set of 46 State Transport Undertakings of India have been considered for the study. DEA was applied to compute the efficiencies of units under study. Potential improvements in the input and output variables were computed for the inefficient units. Regression analysis was then performed to identify the explanatory variables that significantly affect the input and output variables. It was observed that DTC is one amongst the worst performers. It showed a technical inefficiency of 50.94% and was operating on decreasing returns to scale. Further, DTC needs to increase its output substantially in order to attain the level of efficiency. Also it is not utilizing its resources optimally as it needs to reduce all its inputs. In other words, DTC is not utilizing its resources as optimally as its efficient peers. This paper is an attempt to apply regression technique along with the non parametric technique of Data Envelopment Analysis so that the decision makers of DTC can identify the areas where improvement is required and plan a strategy to improve their performance. This would enable DTC to move from a loss incurring to a profit making unit.

scholarly journals MEASURING EFFICIENCY IN TRANSPORT: THE STATE OF THE ART OF APPLYING DATA ENVELOPMENT ANALYSIS / TRANSPORTO EFEKTYVUMO NUSTATYMAS: NAUJAUSI DUOMENYS ATLIEKANT PAVIRŠINĘ DUOMENŲ ANALIZĘ / ОПРЕДЕЛЕНИЕ ЭФФЕКТИВНОСТИ ТРАНСПОРТА: НОВЫЕ ДАННЫЕ ПРИ ВЫПОЛНЕНИИ АНАЛИЗА СРЕДЫ ФУНКЦИОНИРОВАНИЯ

Transport ◽  
2011 ◽  
Vol 26 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Rita Markovits-Somogyi
Keyword(s):  
Data Envelopment Analysis ◽  
Best Practice ◽  
Urban Transport ◽  
Transport Sector ◽  
Data Envelopment ◽  
Linear Programming Method ◽  
Non Profit ◽  
Measuring Efficiency ◽  
Dea Models ◽  
Inputs And Outputs

Data Envelopment Analysis (DEA) is a non-parametric linear programming method used for determining the efficiency of a set of companies as compared to the best practice frontier. It can be employed to analyze private, public or non-profit organizations. The application of the method in the transport sector is wide-spread, especially in the evaluation of airports, ports, railways and urban transport companies. The present paper is aimed at giving a review of how DEA is applied in the transport sector with a special emphasis on the inputs and outputs selected in the DEA models employed in different fields. For this reason, the author has compiled data from 69 DEA applications found or reported in literature, investigated their characteristics and the field of applying them along with the inputs and outputs used. Santrauka Paviršinė duomenų analizė (Data Envelopment Analysis, DEA)—neparametrinis linijinio programavimo metodas, taikomas siekiant nustatyti įmonių grupės efektyvumą, lyginant jį su geriausiais praktiniais laimėjimais. Jis gali būti įgyvendinamas vertinant privačių, valstybinių ir ne pelno siekiančių organizacijų veiklą. Šis metodas plačiai paplitęs transporto sektoriuje, ypač vertinant oro uostų, geležinkelių ir viešojo transporto organizacijas. Straipsnyje nagrinėjama, kaip DEA metodas taikomas transporto sektoriuje, ir pabrėžiamos įdėtos pastangos bei pasiekti rezultatai, naudojant DEA modelius skirtingose srityse. Surinkti duomenys iš DEA modelių ir išnagrinėtos jų charakteristikos. Резюме Анализ среды функционирования (Data Envelopment Analysis – DEA) – это метод сравнительного анализа деятельности различных сложных экономических и социальных систем. Метод анализа среды функционирования охватывает гораздо более широкий спектр понятий и возможностей, чем просто вычисление и анализ эффективности сложных объектов. Метод имеет глубокую связь с математической экономикой, системным анализом, многокритериальной оптимизацией, он позволяет строить многомерное экономическое пространство, находить оптимальные пути развития в нем, вычислять важнейшие количественные и качественные характеристики поведения объектов, моделировать различные ситуации. Этот метод может быть использован при оценке деятельности частных и государственных предприятий и организаций. Метод также применяется в транспортной сфере для оценки деятельности аэропортов, предприятий железнодорожного и общественного транспорта. В статье исследуется применение метода анализа среды функционирования в транспортной сфере. Анализируются данные, характеристики и результаты различных исследований с применением метода анализа среды функционирования.

scholarly journals Model of Determining the Optimal, Green Transport Route among Alternatives: Data Envelopment Analysis Settings

2020 ◽  
Vol 8 (10) ◽  
pp. 735
Author(s):  
Luka Vukić ◽  
Tanja Poletan Jugović ◽  
Giambattista Guidi ◽  
Renato Oblak
Keyword(s):  
Data Envelopment Analysis ◽  
Optimal Transport ◽  
Transport Cost ◽  
Transport Sector ◽  
Practical Case ◽  
Data Envelopment ◽  
Quantitative Factor ◽  
Shortest Route ◽  
Starting Point ◽  
Set Up

Data envelopment analysis (DEA) is a useful method for determining relative efficiency in many types of businesses, including the transport sector. In line with the European Union’s (EU) policy of sustainable development of transport, external costs become the competitiveness factor of the transport route valorization. Presenting specific DEA settings, the paper aims to show and test a developed model for determining the optimal transport route among alternatives towards the same destination where external cost as a socio-ecological factor is included in DEA, along with transport cost (quantitative factor) and transport time (qualitative factor). In order to adhere to the principles of the least possible energy consumption, the given distance that also included in DEA settings represents the shortest route between the starting point and destination, as a unique and constant output variable. Therefore, the optimal direction selected by the DEA stands for the green route. The capabilities of the DEA, set up in this way within the broader model, are demonstrated in the practical case.

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