Research Into the Methods of Analysing the Productivity Indicators of Transport Terminals

Jaržemskienė, Ilona

doi:10.3846/1648-4142.2009.24.192-199

Cited by 5 publications

(6 citation statements)

References 54 publications

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“…The findings of the previous research effort in the light of the latest developments within D.E.A. applications in transport were presented in Jarzemskiene's article (Jaržemskienė, 2009). Meta-analysis of DEA applications was carried also out by Odeck and Brathen (Odeck & Bråthen, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Upgraded data envelopment analysis model application for total productivity comparison in major airports of the European Union

Jaržemskis

Jaržemskienė

2018

Economic Research-Ekonomska Istraživanja

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In order to compare the productivity of airports different scientific approaches are used by various authors. Previous research has shown wide application of the data envelopment analysis (D.E.A.) model for productivity comparison. The D.E.A. model may be used to compare both partial and total factor productivity. However, aggregation of the separate partial factors into a single D.E.A. model is still not sufficiently investigated. The purpose of this paper is to suggest the integration of two methods -DEA and Delphi Expert Panel -to solve this problem. A model was developed and experimentally tested with 15 major European Union airports. The results show that the suggested model could be efficiently used to compare the airports' productivity, which is expressed by a large set of attributes. The main conclusion is that model can be successfully used to compare airports by different criteria through integrating the DEA and Delphi Expert Panel techniques. The model can be used for any set of airports to compare productivity. The research could be useful to airport managers and investors, as well as to researchers in the area of D.E.A. application.

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Section: Literature Reviewmentioning

confidence: 99%

Upgraded data envelopment analysis model application for total productivity comparison in major airports of the European Union

Jaržemskis

Jaržemskienė

2018

Economic Research-Ekonomska Istraživanja

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“…The author of the article discusses the theoretical assumptions of the DEA method used for measuring the productivity of airports explained in the article 'Research into the methods of analysing the productivity indicators of transport terminals' (Jaržemskienė 2009). The article deals with the findings of the study carried out in accordance with those assumptions.…”

Section: Introductionmentioning

confidence: 99%

Applying the Method of Measuring Airport Productivity in the Baltic Region

Jaržemskienė

2012

Transport

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The author of the article describes the theoretical assumptions of the DEA method used for measuring the productivity of airports described in the article ‘Research into the methods of analysing the productivity indicators of transport terminals’ (Jaržemskienė 2009). The essential insights presented in the above mentioned paper reveal that Data Envelopment Analysis (DEA) is a relatively new ‘data oriented’ approach to evaluating the performance of the so-called Decision Making Units (DMU) that convert multiple inputs into multiple outputs. The article focuses on the findings of the study carried out by the author in accordance with those assumptions. Research represents the Data Envelopment Analysis (DEA) method testing 15 selected airports situated in the Baltic Region, including Vilnius, Kaunas, Palanga, Riga, Tallinn, St. Petersburg, Helsinki, Turku, Stockholm, Malme, Copenhagen, Hamburg, Gdansk, Warsaw and Minsk. Airport productivity indicators are ranked considering importance and using the method of Delphi expert survey made of two rounds. The author presented the following indicators (expressed as ‘ratio’) as the major ones estimated by PAX/LAND, AIR/LAND, PAX/AIR, PAX/RW, PAX/RWA, GA and INTER experts. The succeeding indicators were introduced by PAX/TERMAREA, PAX/GATES, AIR/RW, AIR/RWA, AIR/TERMAREA, AIR/GATES, FR/LAND and FR/RW. 10 indicators were accepted as the most important and selected from the current set in the following sequence: AIR/LAND, AIR/RW, PAX/RW, PAX/LAND, AIR/RWA, PAX/AIR, PAX/RWA, AIR/TERMAREA, PAX/GATES and PAX/TERMAREA. AIR/LAND and AIR/RW were submitted as two main indicators. The acronyms are explained as follows: LAND – airport area, RWA – runway length, PAX – the number of passengers, AIR – the number of aircraft take-offs and landings, RW – the number of runways, GATES – the number of gates, FR – the amount of freight served, TERMAREA – the area of passenger terminal, GA – a general aviation market share of airport served aircraft by percentage, INTER – the percentage of international passengers considering all passengers served by airports. After two key productivity indicators were chosen conducting the expert survey, airport productivity was compared applying the DEA method.

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“…For DEA, it is a nonparametric method of measuring the relative efficiency of a decision making unit such as a firm or public-sector agency, which results in estimating actual as well as potential efficiency. Ranking based on such efficiency is usually not very robust (Jaržemskienė 2009). Nevertheless, DEA offers some additional information that soundly supports multi-criteria optimization.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Efficiency of Lithuanian Transport Sector by Applying the Methods of Multimoora and Data Envelopment Analysis

Baležentis

2011

Transport

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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.

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Research Into the Methods of Analysing the Productivity Indicators of Transport Terminals

Cited by 5 publications

References 54 publications

Upgraded data envelopment analysis model application for total productivity comparison in major airports of the European Union

Upgraded data envelopment analysis model application for total productivity comparison in major airports of the European Union

Applying the Method of Measuring Airport Productivity in the Baltic Region

Assessing the Efficiency of Lithuanian Transport Sector by Applying the Methods of Multimoora and Data Envelopment Analysis

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