EVALUATION OF UNCONSTRAINING METHODS IN AIRLINES’ REVENUE MANAGEMENT SYSTEMS

Airline revenue management systems are used to calculate booking limits on each fare class to maximize expected revenue for all future flight departures. Their performance depends critically on the forecasting module that uses historical data to project future quantities of demand. Those data are censored or constrained by the imposed booking limits and do not represent true demand since rejected requests are not recorded. Eight unconstraining methods that transform the censored data into more accurate estimates of actual historical demand ranging from naive methods such as discarding all censored observation, to complex, such as Expectation Maximization Algorithm and Projection Detruncation Algorithm, are analyzed and their accuracy is compared. Those methods are evaluated and tested on simulated data sets generated by ICE V2.0 software: first, the data sets that represent true demand were produced, then the aircraft capacity was reduced and EMSRb booking limits for every booking class were calculated. These limits constrained the original demand data at various points of the booking process and the corresponding censored data sets were obtained. The unconstrained methods were applied to the censored observations and the resulting unconstrained data were compared to the actual demand data and their performance was evaluated

A Review of Ancillary Services Implementation in the Revenue Management Systems

Ancillary services in air transport represent a set of services provided to passengers to choose from, enabling them to enhance their travel experience while accumu-lating additional airline revenue. Low-cost airlines pi-oneered the practice, but the separation of ancillary services from the basic service has become an intense-ly growing trend in the air transport industry over the last decade. This practice has enabled low-cost airlines to significantly reduce the price of the basic service. To remain competitive in an era of transparency provided by search engines, traditional airlines offer ancillary ser-vices in addition to the basic service. To meet the passen-ger’s needs, a whole range of ancillary services has been created. However, existing revenue management systems do not take this ancillary revenue into account when cal-culating reservation limits. If the airline knew that an in-dividual passenger is willing to pay more for ancillary services, the system would be able to adjust the availabil-ity of the service for that passenger during the booking process. A review of research on passengers’ willingness to pay for ancillary services is presented in the paper, as well as a review on research on the personalisation of ancillary services and challenges of integrating person-alised pricing into existing revenue management systems

Temporal Causal Model for Passenger Air Traffic Recovery During Post-recession Periods

Industrija zračnog prijevoza se, uslijed pandemije bolesti COVID-19 i nametnutih ograničenja putovanja, suočila s dubokom gospodarskom krizom i posljedičnim smanjenjem potražnje koje je intenzivnije od svih ranijih recesija. U radu se istražuje uzročna veza između potražnje za zračnim prijevozom i relevantnih društveno-ekonomskih pokazatelja kako bi se kvantificirao budući rast industrije zračnog prijevoza, s posebnim naglaskom na oporavak nakon recesija bez obzira na njihov uzrok, obuhvat i trajanje. Na temelju robusnih setova povijesnih podataka koji pokrivaju period od 1990. do 2022. godine, a time i periode ranijih značajnih recesija koje su imale utjecaj na industriju zračnog prometa, analizira se uzročna veza između pokazatelja ostvarenoga putničkog zračnog prometa i drugih prometnih indikatora te niza društveno-ekonomskih pokazatelja koji su imali značajan utjecaj na dinamiku gospodarskih trendova u razdobljima oporavka. U radu su utvrđene uzročne veze između pokazatelja zračnog prometa i svih istraživanih društveno-ekonomskih pokazatelja. Ta je uzročna veza različita u različitim promatranim razdobljima i nije uvijek jednako značajna. U razdobljima recesija neki društveno-ekonomski pokazatelji snažnije uzrokuju kretanja prometnih pokazatelja, dok su u razdobljima ekspanzija značajniji drugi društveno-ekonomski pokazatelji. Predložen je vremenski uzročni model za prognoziranje oporavka putničkoga zračnog prometa koji uzima u obzir niz prometnih i društveno-ekonomskih pokazatelja i njihove uzročne veze. Modelom se kreira povezani sustav vremenskih nizova koji uključuje samo one prediktore koji pokazuju značajan utjecaj na odabrane pokazatelje prometne potražnje u zračnom prometu. Model pruža kvalitativnu procjenu uzročnih veza i dopušta razumijevanje uzročne strukture između varijabli. Prognoze se postižu primjenom modela koji je izveden iz uzročne strukture i upotrebljava podatke prošlih vrijednosti prediktorske i target varijable kako bi se predvidjele buduće vrijednosti odabranoga vremenskog niza. Izrađene su prognoze prometne potražnje u putničkome i teretnome zračnom prometu za sljedećih 12 mjeseci čije su performanse uspoređene s ARIMA modelom kojim se prognozira isti period. Isto se tako uspoređuju ostvareni rezultati u putničkome zračnom prometu u prognoziranom periodu s rezultatima prognoza dobivenih vremenskim uzročnim modelom i ARIMA modelom. Na temelju rezultata i analize dobre prakse predložene su smjernice za brži oporavak zračnih prijevoznika nakon recesije. Stečene spoznaje pridonose optimizaciji prometno-tehnoloških procesa poslovanja zračnih prijevoznika u postkriznim razdobljima.The global air transport industry, an integral component of the world economy, has faced an unprecedented economic crisis characterized by a substantial reduction in demand. This reduction is more intense than any previous recessions the industry has encountered, and it is primarily attributed to the impact of the COVID-19 pandemic and the stringent travel restrictions that followed in its wake. This study explores the causal relationship between air transport demand and pertinent socio-economic indicators, with the aim of quantifying the future growth prospects of the air transport sector. It places particular emphasis on understanding the dynamics of recovery after recessions, regardless of their underlying causes, scope, or duration. Drawing upon a dataset spanning from 1990 to 2022, which encompasses periods of significant recessions that have left a notable imprint on the air transport industry, this research meticulously examines the causal links between passenger air transport indicators, other key transportation metrics, and a diverse array of socio-economic indicators that wield substantial influence over economic trends during post-recession recovery phases. The findings of this study reveal the existence of discernible causal relationships between air transport indicators and socio-economic indicators. However, these causal relationships exhibit a dynamic nature, manifesting variations across different observed periods. Notably, during recessions, specific socio-economic indicators exert a more pronounced influence on air transportation metrics. In contrast, during economic expansion periods, alternative socio-economic factors come to the fore, indicating a shifting landscape of causality that necessitates adaptive strategies. To facilitate precise forecasting of passenger air traffic recovery, this research introduces a temporal causal model. This model represents a systematic approach that integrates various transportation and socio-economic indicators while considering their causal relationships. It strategically selects predictors that exhibit significant impacts on the chosen air transport indicators, thereby enhancing the accuracy of forecasts and providing decision-makers with actionable insights. Using the time-causal model, a system of time-series has been created, allowing for the identification of causal relationships between selected time-series at a much more complex level than the bivariate Granger causality test. The time-causal model includes the use of autoregressive models, vector autoregressive models (VAR), vector error correction models (VECM), and the Granger causality test. Air transport indicators are assigned the role of predictor and target variables simultaneously, while socio-economic indicators are assigned the role of predictor variables. It was determined that there is a difference in causal relationships during periods of expansion and recession. During recession periods, indicators such as the price of crude oil, jet fuel, and ownership rates have a stronger influence on air transport indicators, whereas during expansion periods, the number of employees in the air transport industry and airline ticket prices have a stronger influence on air transport indicators. Some socio-economic indicators, such as population and gross domestic product, affect air transport indicators in both expansion and recession periods, although not necessarily with the same intensity. Forecasted values from the temporal causal and ARIMA models were compared with realized traffic to assess the quality and accuracy of the models. Based on the comparison the time-causal model enables instant analysis of circumstances and causes in dynamic environments, as well as reliable forecasting of upcoming periods. In the aftermath of economic downturns, airlines face the formidable challenge of recovery. Building on empirical evidence and rigorous analysis, this research culminates in the formulation of pragmatic guidelines. These guidelines provide a structured framework for airlines to navigate the turbulent post-recession landscape more effectively. By optimizing operational processes, refining marketing strategies, and fostering collaboration among industry stakeholders, these recommendations aim to expedite and fortify the recovery process. This comprehensive study sheds light on the intricate dynamics within the global air transport industry during times of crisis and recovery. It equips industry stakeholders with a powerful forecasting tool, the temporal causal model, to make informed decisions in an ever-changing landscape. By unraveling the multifaceted causal relationships and offering practical recovery guidelines, this research contributes significantly to the body of knowledge in the field of air transportation economics and management. The acquired insights pave the way for informed decision-making and sustainable growth in the industry

Temporal Causal Model for Passenger Air Traffic Recovery During Post-recession Periods

Industrija zračnog prijevoza se, uslijed pandemije bolesti COVID-19 i nametnutih ograničenja putovanja, suočila s dubokom gospodarskom krizom i posljedičnim smanjenjem potražnje koje je intenzivnije od svih ranijih recesija. U radu se istražuje uzročna veza između potražnje za zračnim prijevozom i relevantnih društveno-ekonomskih pokazatelja kako bi se kvantificirao budući rast industrije zračnog prijevoza, s posebnim naglaskom na oporavak nakon recesija bez obzira na njihov uzrok, obuhvat i trajanje. Na temelju robusnih setova povijesnih podataka koji pokrivaju period od 1990. do 2022. godine, a time i periode ranijih značajnih recesija koje su imale utjecaj na industriju zračnog prometa, analizira se uzročna veza između pokazatelja ostvarenoga putničkog zračnog prometa i drugih prometnih indikatora te niza društveno-ekonomskih pokazatelja koji su imali značajan utjecaj na dinamiku gospodarskih trendova u razdobljima oporavka. U radu su utvrđene uzročne veze između pokazatelja zračnog prometa i svih istraživanih društveno-ekonomskih pokazatelja. Ta je uzročna veza različita u različitim promatranim razdobljima i nije uvijek jednako značajna. U razdobljima recesija neki društveno-ekonomski pokazatelji snažnije uzrokuju kretanja prometnih pokazatelja, dok su u razdobljima ekspanzija značajniji drugi društveno-ekonomski pokazatelji. Predložen je vremenski uzročni model za prognoziranje oporavka putničkoga zračnog prometa koji uzima u obzir niz prometnih i društveno-ekonomskih pokazatelja i njihove uzročne veze. Modelom se kreira povezani sustav vremenskih nizova koji uključuje samo one prediktore koji pokazuju značajan utjecaj na odabrane pokazatelje prometne potražnje u zračnom prometu. Model pruža kvalitativnu procjenu uzročnih veza i dopušta razumijevanje uzročne strukture između varijabli. Prognoze se postižu primjenom modela koji je izveden iz uzročne strukture i upotrebljava podatke prošlih vrijednosti prediktorske i target varijable kako bi se predvidjele buduće vrijednosti odabranoga vremenskog niza. Izrađene su prognoze prometne potražnje u putničkome i teretnome zračnom prometu za sljedećih 12 mjeseci čije su performanse uspoređene s ARIMA modelom kojim se prognozira isti period. Isto se tako uspoređuju ostvareni rezultati u putničkome zračnom prometu u prognoziranom periodu s rezultatima prognoza dobivenih vremenskim uzročnim modelom i ARIMA modelom. Na temelju rezultata i analize dobre prakse predložene su smjernice za brži oporavak zračnih prijevoznika nakon recesije. Stečene spoznaje pridonose optimizaciji prometno-tehnoloških procesa poslovanja zračnih prijevoznika u postkriznim razdobljima.The global air transport industry, an integral component of the world economy, has faced an unprecedented economic crisis characterized by a substantial reduction in demand. This reduction is more intense than any previous recessions the industry has encountered, and it is primarily attributed to the impact of the COVID-19 pandemic and the stringent travel restrictions that followed in its wake. This study explores the causal relationship between air transport demand and pertinent socio-economic indicators, with the aim of quantifying the future growth prospects of the air transport sector. It places particular emphasis on understanding the dynamics of recovery after recessions, regardless of their underlying causes, scope, or duration. Drawing upon a dataset spanning from 1990 to 2022, which encompasses periods of significant recessions that have left a notable imprint on the air transport industry, this research meticulously examines the causal links between passenger air transport indicators, other key transportation metrics, and a diverse array of socio-economic indicators that wield substantial influence over economic trends during post-recession recovery phases. The findings of this study reveal the existence of discernible causal relationships between air transport indicators and socio-economic indicators. However, these causal relationships exhibit a dynamic nature, manifesting variations across different observed periods. Notably, during recessions, specific socio-economic indicators exert a more pronounced influence on air transportation metrics. In contrast, during economic expansion periods, alternative socio-economic factors come to the fore, indicating a shifting landscape of causality that necessitates adaptive strategies. To facilitate precise forecasting of passenger air traffic recovery, this research introduces a temporal causal model. This model represents a systematic approach that integrates various transportation and socio-economic indicators while considering their causal relationships. It strategically selects predictors that exhibit significant impacts on the chosen air transport indicators, thereby enhancing the accuracy of forecasts and providing decision-makers with actionable insights. Using the time-causal model, a system of time-series has been created, allowing for the identification of causal relationships between selected time-series at a much more complex level than the bivariate Granger causality test. The time-causal model includes the use of autoregressive models, vector autoregressive models (VAR), vector error correction models (VECM), and the Granger causality test. Air transport indicators are assigned the role of predictor and target variables simultaneously, while socio-economic indicators are assigned the role of predictor variables. It was determined that there is a difference in causal relationships during periods of expansion and recession. During recession periods, indicators such as the price of crude oil, jet fuel, and ownership rates have a stronger influence on air transport indicators, whereas during expansion periods, the number of employees in the air transport industry and airline ticket prices have a stronger influence on air transport indicators. Some socio-economic indicators, such as population and gross domestic product, affect air transport indicators in both expansion and recession periods, although not necessarily with the same intensity. Forecasted values from the temporal causal and ARIMA models were compared with realized traffic to assess the quality and accuracy of the models. Based on the comparison the time-causal model enables instant analysis of circumstances and causes in dynamic environments, as well as reliable forecasting of upcoming periods. In the aftermath of economic downturns, airlines face the formidable challenge of recovery. Building on empirical evidence and rigorous analysis, this research culminates in the formulation of pragmatic guidelines. These guidelines provide a structured framework for airlines to navigate the turbulent post-recession landscape more effectively. By optimizing operational processes, refining marketing strategies, and fostering collaboration among industry stakeholders, these recommendations aim to expedite and fortify the recovery process. This comprehensive study sheds light on the intricate dynamics within the global air transport industry during times of crisis and recovery. It equips industry stakeholders with a powerful forecasting tool, the temporal causal model, to make informed decisions in an ever-changing landscape. By unraveling the multifaceted causal relationships and offering practical recovery guidelines, this research contributes significantly to the body of knowledge in the field of air transportation economics and management. The acquired insights pave the way for informed decision-making and sustainable growth in the industry

Temporal Causal Model for Passenger Air Traffic Recovery During Post-recession Periods

Industrija zračnog prijevoza se, uslijed pandemije bolesti COVID-19 i nametnutih ograničenja putovanja, suočila s dubokom gospodarskom krizom i posljedičnim smanjenjem potražnje koje je intenzivnije od svih ranijih recesija. U radu se istražuje uzročna veza između potražnje za zračnim prijevozom i relevantnih društveno-ekonomskih pokazatelja kako bi se kvantificirao budući rast industrije zračnog prijevoza, s posebnim naglaskom na oporavak nakon recesija bez obzira na njihov uzrok, obuhvat i trajanje. Na temelju robusnih setova povijesnih podataka koji pokrivaju period od 1990. do 2022. godine, a time i periode ranijih značajnih recesija koje su imale utjecaj na industriju zračnog prometa, analizira se uzročna veza između pokazatelja ostvarenoga putničkog zračnog prometa i drugih prometnih indikatora te niza društveno-ekonomskih pokazatelja koji su imali značajan utjecaj na dinamiku gospodarskih trendova u razdobljima oporavka. U radu su utvrđene uzročne veze između pokazatelja zračnog prometa i svih istraživanih društveno-ekonomskih pokazatelja. Ta je uzročna veza različita u različitim promatranim razdobljima i nije uvijek jednako značajna. U razdobljima recesija neki društveno-ekonomski pokazatelji snažnije uzrokuju kretanja prometnih pokazatelja, dok su u razdobljima ekspanzija značajniji drugi društveno-ekonomski pokazatelji. Predložen je vremenski uzročni model za prognoziranje oporavka putničkoga zračnog prometa koji uzima u obzir niz prometnih i društveno-ekonomskih pokazatelja i njihove uzročne veze. Modelom se kreira povezani sustav vremenskih nizova koji uključuje samo one prediktore koji pokazuju značajan utjecaj na odabrane pokazatelje prometne potražnje u zračnom prometu. Model pruža kvalitativnu procjenu uzročnih veza i dopušta razumijevanje uzročne strukture između varijabli. Prognoze se postižu primjenom modela koji je izveden iz uzročne strukture i upotrebljava podatke prošlih vrijednosti prediktorske i target varijable kako bi se predvidjele buduće vrijednosti odabranoga vremenskog niza. Izrađene su prognoze prometne potražnje u putničkome i teretnome zračnom prometu za sljedećih 12 mjeseci čije su performanse uspoređene s ARIMA modelom kojim se prognozira isti period. Isto se tako uspoređuju ostvareni rezultati u putničkome zračnom prometu u prognoziranom periodu s rezultatima prognoza dobivenih vremenskim uzročnim modelom i ARIMA modelom. Na temelju rezultata i analize dobre prakse predložene su smjernice za brži oporavak zračnih prijevoznika nakon recesije. Stečene spoznaje pridonose optimizaciji prometno-tehnoloških procesa poslovanja zračnih prijevoznika u postkriznim razdobljima.The global air transport industry, an integral component of the world economy, has faced an unprecedented economic crisis characterized by a substantial reduction in demand. This reduction is more intense than any previous recessions the industry has encountered, and it is primarily attributed to the impact of the COVID-19 pandemic and the stringent travel restrictions that followed in its wake. This study explores the causal relationship between air transport demand and pertinent socio-economic indicators, with the aim of quantifying the future growth prospects of the air transport sector. It places particular emphasis on understanding the dynamics of recovery after recessions, regardless of their underlying causes, scope, or duration. Drawing upon a dataset spanning from 1990 to 2022, which encompasses periods of significant recessions that have left a notable imprint on the air transport industry, this research meticulously examines the causal links between passenger air transport indicators, other key transportation metrics, and a diverse array of socio-economic indicators that wield substantial influence over economic trends during post-recession recovery phases. The findings of this study reveal the existence of discernible causal relationships between air transport indicators and socio-economic indicators. However, these causal relationships exhibit a dynamic nature, manifesting variations across different observed periods. Notably, during recessions, specific socio-economic indicators exert a more pronounced influence on air transportation metrics. In contrast, during economic expansion periods, alternative socio-economic factors come to the fore, indicating a shifting landscape of causality that necessitates adaptive strategies. To facilitate precise forecasting of passenger air traffic recovery, this research introduces a temporal causal model. This model represents a systematic approach that integrates various transportation and socio-economic indicators while considering their causal relationships. It strategically selects predictors that exhibit significant impacts on the chosen air transport indicators, thereby enhancing the accuracy of forecasts and providing decision-makers with actionable insights. Using the time-causal model, a system of time-series has been created, allowing for the identification of causal relationships between selected time-series at a much more complex level than the bivariate Granger causality test. The time-causal model includes the use of autoregressive models, vector autoregressive models (VAR), vector error correction models (VECM), and the Granger causality test. Air transport indicators are assigned the role of predictor and target variables simultaneously, while socio-economic indicators are assigned the role of predictor variables. It was determined that there is a difference in causal relationships during periods of expansion and recession. During recession periods, indicators such as the price of crude oil, jet fuel, and ownership rates have a stronger influence on air transport indicators, whereas during expansion periods, the number of employees in the air transport industry and airline ticket prices have a stronger influence on air transport indicators. Some socio-economic indicators, such as population and gross domestic product, affect air transport indicators in both expansion and recession periods, although not necessarily with the same intensity. Forecasted values from the temporal causal and ARIMA models were compared with realized traffic to assess the quality and accuracy of the models. Based on the comparison the time-causal model enables instant analysis of circumstances and causes in dynamic environments, as well as reliable forecasting of upcoming periods. In the aftermath of economic downturns, airlines face the formidable challenge of recovery. Building on empirical evidence and rigorous analysis, this research culminates in the formulation of pragmatic guidelines. These guidelines provide a structured framework for airlines to navigate the turbulent post-recession landscape more effectively. By optimizing operational processes, refining marketing strategies, and fostering collaboration among industry stakeholders, these recommendations aim to expedite and fortify the recovery process. This comprehensive study sheds light on the intricate dynamics within the global air transport industry during times of crisis and recovery. It equips industry stakeholders with a powerful forecasting tool, the temporal causal model, to make informed decisions in an ever-changing landscape. By unraveling the multifaceted causal relationships and offering practical recovery guidelines, this research contributes significantly to the body of knowledge in the field of air transportation economics and management. The acquired insights pave the way for informed decision-making and sustainable growth in the industry