ARCHIVES OF TRANSPORT ISSN (print): 0866-9546 Volume 47, Issue 3, 2018 e-ISSN (online): 2300-8830 DOI: 10.5604/01.3001.0012.6505

PASSENGER LEVEL OF SERVICE ESTIMATION MODEL FOR QUEUING SYSTEMS AT THE

Artur KIERZKOWSKI1, Tomasz KISIEL2, Maria PAWLAK3 1, 2, 3 Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Wroclaw, Poland

Contact: 2) [email protected] (corresponding author)

Abstract:

This paper presents a model for the management of passenger service operations at by the estimation of a global index of the level of service. This paper presents a new approach to the scheduling of resources required to perform passenger service operations at airports. The approach takes into account the index of level of service as a quantitative indicator that can be associated with airport revenues. Taking this index into account makes it possible to create an operating schedule of desks, adapted to the intensity of checking-in passengers, and, as such, to apply dynamic process management. This offers positive aspects, particularly the possibility of improvement of service quality that directly translates into profits generated by the non-aeronautical activity of airports. When talking about level of service, there can be distinguish other important indicators that are considered very often (eg maximum queuing time, space in square meters). In this model, however, they are considered as secondary. Of course, space in square meters is important when designing a system. Here this system is already built and functioning. The concept of the model is the use of a hybrid method: computer simulation (Monte Carlo simulation) with multiple regression. This paper focuses on the presentation of a mathematical model used to determine the level of service index that provides new functionality in the current simulation model, as presented in the authors’ previous scientific publications. The mathematical model is based on a multiple regression function, taking into account the significance of individual elementary operations of passenger service at an air terminal.

Key words: level of service, queuing system, airport, passenger service

To cite this article: Kierzkowski, A., Kisiel, T., Pawlak, M., 2018. Passenger level of service estimation model for queuing systems at the airport. Archives of Transport, 47(3), 29-38. DOI: https://doi.org/10.5604/01.3001.0012.6505

Article is available in open access and licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0)

30 Kierzkowski, A., Kisiel, T., Pawlak, M., Archives of Transport, 47(3), 29-38, 2018

1. Introduction vital from the process manager’s perspective. Creat- Airports are now expected to provide a high level of ing the right number of stations and counters to be service (LoS) to various customers. From the pas- used to perform individual passenger service opera- senger’s perspective, the most important aspect is tions makes it possible to have a considerable influ- not only the provision of a service to reach the des- ence on passengers’ waiting time in queuing sys- tination, but also the level of service. With this in tems. The aim of the paper was to develop a concept mind, improvement of the level of service at the air- model that would be suitable for analysing the influ- port has become a priority. However, the multiplic- ence of the operating schedule planning of service ity of airport services is the reason why the attain- desks in queuing systems on LoS index changes. ment of efficiency of measurements and analyses of The study involved computer simulations combined passenger perception is not easy. There are methods with a mathematical model based on a linear regres- that facilitate optimisation of the level of service. In sion and multiple regression models and using the practice, however, it is difficult to predict precisely least square method. So far, the research has been passengers’ responses. In order to avoid misinterpre- focused on the evaluation of the current condition of tation of a passenger’s impressions, Bezerra et al. service systems, whereas this paper is oriented to- (2016) proposed two solutions. The first one related wards predicting forthcoming operations in the as- to the adaptation of the measurement model for the pect of decisions to be made in the process of sched- perception of the level of quality based on a typical uling of resources. service provided in air transport. The other solution Addressing this subject is essential in the air was supposed to test the equivalence of the model transport process. The airport service evaluation re- between different passenger groups. In order to ver- port (CAPSE, 2016) concerning Asian airports un- ify correctness of the assumptions, example data ob- ambiguously shows that the level of passengers’ sat- tained a survey conducted in real-system circum- isfaction has been clearly decreasing for the last five stances. The results confirm the soundness of the years. At the same time, there are no analogous re- six-factor structure. The presented model is recog- ports relating to European airports. The significance nised as an alternative to the multidimensional ap- of passenger satisfaction resulting in profits gener- proach in the context of measuring the performance ated by non-aeronautical activity of an airport was of an airport with reference to the level of service. described in DKMA (2014). It was showed that an This is a very significant approach that justifies the evaluation increase by 0.1 on a five-point scale re- analysis of how a passenger terminal functions sults in an increase in the profits generated by non- within the LoS aspect. aviation activity by USD 0.8 per served passenger. For Correia et al. (Correia and Wirasinghe, 2007; The importance of cost management in air transport Correia et al., 2008), a general level of service con- has already been written (Jafernik and Sklorz, 2015; cerning passenger terminals is based on the proce- Jacyna-Gołda, 2015; Żak, 2004, Koucky, 2007; dure that consists in the observation of passengers Vintr, 2007) and acquisition of information that may affect pas- Subsequent passenger service operations performed sengers’ evaluation of the airport. This approach is in series are dependent processes (Kierzkowski and used to obtain quantitative evaluations based on the Kisiel, 2017, 2018). Thus, an appropriate model can survey data, whereas an analysis provides a relation- be used to develop an operating schedule so that the ship of quantitative evaluations and global indices. global index of passenger evaluation at the airport Correia et al. (Correia and Wirasinghe, 2007; Cor- assumes the highest value possible. reia et al., 2008) also determine the level of service at terminals based on users’ opinions. As a ground 2. Queuing systems at the airport for this concept, quantitative values are assigned to By adapting the notation applied in Malarski (2006), individual services with reference to tests conducted the passenger service operation can be expressed as at airports. An analysis showed that the most note- a graph adjusted for use in computer simulation worthy influence on the level of service is exerted modelling. The graph consists of elementary sub- by the following factors: queuing time, service time graphs that take into account an additional variable, and space availability. These are the factors that are on the basis of which it will be possible to determine a passenger’s waiting time in the queue. Hence, each Kierzkowski, A., Kisiel, T., Pawlak, M., 31 Archives of Transport, 47(3),29-38, 2018

elementary graph relates to a subsequent passenger γwe – variable describing random conditions of the service operation, taking into account these varia- duration of operation when the passenger enters the bles: terminal – the variable must assume (here determin- - ψ: variable describing random conditions of the istically) a value equal to 0 (Gwe –graph of an artifi- instant when the passenger appears for a ser- cial operation); vice operation; ξwe – variable describing the consequent of the oper- - γ: variable describing random conditions of the ation, where: time between the operation is started and the 0 - consequent Gwiz tasks included in the operation are finished; ξwe= {1 - consequent Gc-in - ξ: variable describing the consequent of the op- 2 - consequent Gkb eration.

The passenger service operation may be completed we at the airport in different configurations. It depends G represents the operation of a passenger entering on the arrangement of individual zones in the air ter- the terminal – this is not a queuing system. However, a comprehensive approach to passenger service op- minal. Particular relevance is given to operations of we control (ID control) and safety control. For eration G was implemented demonstratively as an instance, safety control may be performed in a cen- artificial passenger service operation. tralised system. If this is the case, the safety control In the PO check-in process, there are different zone is located at the transition between the gener- check-in methods. However, one may generalise by ally-accessible zone and the departure lounge. At assuming that this is a binary operation. The passen- some airports, safety control takes place directly at ger checks in at the airport (independently of the the entrance to the or just in front of the gate. method: service station or counter, self-service ki- At most regional airports, there is a service operation osk, etc.) or the passenger has already completed together with a centralised system as well as pass- check-in before arriving at the passenger terminal. port control that takes place in the departure lounge. For passengers checking in, the operation can be ex- In such a case, the departure lounge is divided into pressed as the following elementary graph: the Schengen zone and a non-Schengen zone. Inde- c-in c-in c-in c-in pendently of the arrangement of individual elements G =(ψ ,γ ,ξ ) (2) of the passenger service system, the service opera- tions can be expressed in the form of basic elemen- where: tary graphs. For further discussion, a system of sub- ψc-in – variable describing random conditions at the sequent operations is assumed; they correspond to instant when the passenger appears in the check-in the passenger service system performed at an inter- queue; national airport with the IATA code: WRO (variable γc-in – variable describing random conditions of the ξ performed according to the arrangement at duration of the check-in operation; Wroclaw Air Port). ξc-in – variable describing the consequent of the op- The airport performs service operations for passen- eration, where gers who arrive (PP) at or depart (PO) from the air- 0 - consequent Gwiz ξc-in= { port. As far as POs are concerned, there is a certain 1 - consequent Gkb set of elementary graphs that represent subsequent passenger service operations. By analogy to Malar- For passengers required to hold a visa to be allowed ski (2006), PO service commences when the passen- to cross borders, if the connection is within the ger appears at the passenger terminal. An elementary Schengen Area, a visa control operation must be per- graph of the operation can be expressed as follows: formed:

we we we we ( ) (1) wiz wiz G = ψ ,γ ,ξ G =(ψwiz,γwiz,ξ ) (3)

where: where: ψwe – variable describing random conditions of the instant when the passenger appears at the terminal; 32 Kierzkowski, A., Kisiel, T., Pawlak, M., Archives of Transport, 47(3), 29-38, 2018

ψwiz – variable describing random conditions at the air terminal. At airports, there are three basic board- instant when the passenger appears in the visas con- ing methods applied. The main method means that trol operation; passengers board the plane parked on the apron. An- γwiz – variable describing random conditions of the other method involves transporting passengers from duration of the visa control operation; the air terminal by transfer bus. In the third method, ξwiz – variable describing the consequent of the op- passengers access the plane directly by walking eration (the value assumes 0 and is the consequent through a . The passenger opera- Gkb) tion may also be approached as a queuing system due to the fact that the operation begins with check- The safety control operation is performed for every ing and the passenger waits in the passenger who starts their journey at a given airport. queue until the operation is commenced. The analy- This is a key element in the system because passen- sis will involve the moment when the passenger ap- gers service streams of all flights combine. The pears in the queue and the total boarding time (after safety control operation can be expressed as the fol- commencing the boarding pass control). In such a lowing elementary graph: case, the passenger boarding operation can be ex- pressed as the following elementary graph: Gkb=(ψkb,γkb,ξkb) (4) b b b b G =(ψ ,γ ,ξ ) (6) where: ψkb – variable describing random conditions at the where: instant when the passenger appears in the safety con- ψb – variable describing random conditions at the in- trol operation; stant when the passenger appears in the operation of γkb – variable describing random conditions of the boarding pass control; duration of the safety control operation; γb – variable describing random conditions of the du- ξkb – variable describing the consequent of the oper- ration of the passenger boarding the plane; ation, where ξb – variable describing the consequent of the oper- kp ation (the value assumes 0 and means exit from the kb 0 - consequent G ξ = { b system). 1 - consequent G It is possible to distinguish an analogous set of ele- The passengers travelling to countries outside the mentary graphs for PPs. However, one must bear in Schengen Area must pass ID control in order to be mind that the airport can also handle transit passen- allowed to cross borders. The passport control oper- gers (PT) as well as transfer passengers (PTR). The ation can be expressed as the following elementary PT and PTR transport includes intermediate landing, graph: which means that the passenger arrives at an airport and departs from the airport while still boarded on kp kp kp kp G =(ψ ,γ ,ξ ) (5) the same plane. For PTRs, there is a change of the plane. For PPs, PTs and PTRs, a general elementary where: graph of process commencement (arrival at the air- ψkp – variable describing random conditions at the port) can be expressed. The moment when the plane instant when the passenger appears in the passport stops on the apron is used as entrance into the sys- control operation; tem. The system entrance event can also be recog- γkp – variable describing random conditions of the nised as a queuing system. The queuing time can duration of the passport control operation; mean the period from the moment when the plane ξkp – variable describing the consequent of the oper- stops on the apron to the moment when the passen- ation (the value assumes 0 and is the consequent Gb) ger leaves the plane. Next, the passenger system en- trance operation means the time the passenger needs The last operation performed for a PO is boarding; to walk to the passenger terminal. In this case, meth- this operation completes the passenger service at the ods analogous to the passenger boarding operation are applied. For POs, PTs and PTRs, the entrance Kierzkowski, A., Kisiel, T., Pawlak, M., 33 Archives of Transport, 47(3),29-38, 2018

operation can be expressed as the following elemen- It is also assumed that the reclaim operation tary graph: will be expressed by means of the queuing system, where the passenger approaches the Gp=(ψp,γp,ξp) (7) station to wait for their baggage. The baggage re- claim operation can be described by the following where: elementary graph: ψp – random moment when the plane stops on the ob ob apron. G =(ψob,γob,ξ ) (9) γp – variable describing random conditions of the du- kpp ration when the passenger leaves the plane and whereψ – variable describing random conditions walks to the passenger terminal; at the instant when the passenger appears the bag- ξp – variable describing the consequent of the oper- gage system; kpp ation, where γ – variable describing random conditions of the 0 - end of process (PP) duration of the baggage reclaim operation; ξkpp – variable describing the consequent of the op- 1 - consequent Gkpp (PP, PTR) eration (the value assumed 0 and means exit from p ob ξ = 2 - consequent G (PP) the system). kb 3 - consequent G (PTR) By analysing the values assumed by subsequent var- { 4 - consequent Gb ( PT) iables ξ, it is possible to develop a diagram of pas- senger service at an airport Earlier in the paper, it Upon entrance to the terminal, PPs travelling within was mentioned that the passenger service operation the Schengen Area may leave and finish the passen- was presented for Wroclaw Airport, and thus the di- ger service operation. If the passenger travels with agram of that operation is shown in Fig. 1. hold baggage, the passenger appears the queueing The POs, PPs, PTs and PTRs have different passen- system to reclaim their baggage. On arrival, PPs and ger service operation graphs that may be expressed PTRs travelling within the non-Schengen Area un- by a superposition of elementary subgraphs (10-14). dergo passport control. The PTRs travelling within Gwewe(ψ we ,γ we ,ξ, ) the Schengen Area go through safety control. It is  assumed that the PTRs who leave the terminal and c-inc-in c-in c-in G ψ ,γ ,ξ, return to the terminal to continue their journey are ( ) sequentially recognised as PP and PO. The PTs stay Gwizwizψ wiz ,γ wiz ,ξ, ( ) at the departure lounge, and hence they perform the GPO (Ψ,Γ,Ξ) = (10) Gkbkbψ kb ,γ kb ,ξ, boarding operation. ( ) The elementary graph of the passport control opera- Gkpkpψ kp ,γ kp ,ξ, tion on arrival will be analogous to the operation ( ) used for POs, but with a different variable ξ: Gbb(ψ b ,γ b ,ξ)  Gkpp=(ψkpp,γkpp,ξkpp) (8) Gpp(ψ p ,γ p ,ξ) , 

G (Ψ,Γ,Ξ) = Gψkppkpp ,γ kpp ,ξ, kpp (11) whereψkpp – variable describing random conditions PP ( ) at the instant when the passenger appears in the pass- Gobob(ψ ob ,γ ob ,ξ ) port control operation;  γkpp – variable describing random conditions of the Gp(ψ p ,γ p ,ξ p ) , duration of the passport control operation;  kpp Gkppψ kpp ,γ kpp ,ξ kpp , ξ – variable describing the consequent of the op- ( ) eration, where G Ψ,Γ,Ξ =Gkbψ kb ,γ kb ,ξ kb , (12) 0 - end of process (PP) PTR ( ) ( ) kpp 1 - consequent Gob (PP) Gkpψ kp ,γ kp ,ξ kp , ξ = { ( ) kb 2 - consequent G (PTR) b b b b G (ψ ,γ ,ξ ) 34 Kierzkowski, A., Kisiel, T., Pawlak, M., Archives of Transport, 47(3), 29-38, 2018

Fig. 1. Diagram of passenger service operation at WRO

Gpppp(ψ ,γ ,ξ, ) flight timetable and resources he plans to allocate to  the service. The model also identifies passenger GPTRZ (Ψ,Γ,Ξ) = (13) bbbb structures that describe each flight. In response, the G (ψ ,γ ,ξ ) user gets a quantitative rating of the system by the In order to minimise the number of possible graphs passenger. of passenger service operations, the presented It is essential to define profiles for those passengers graphs (10-13) assume that the passenger performs with access to different terminal zones and those all subsequent elementary operations. For passen- who differ in experience and the purpose of air trav- gers whose service excludes individual elementary elling. Based on the knowledge gained by the ex- operations, the variables ψ and γ will not be assigned perts employed at the airport and the available liter- and such operations will be rejected. ature (Caves and Pickard, 2000, de Barros et al., 2007; Hongwei and Yauha, 2016; Martel and Sen- 3. Model of LoS evaluation at the airport evirante, 1990), the following division of passengers The objective of this paper is to design a model that into groups is proposed: PP, PO, PTR, PTRZ – di- will provide logistic support in the management of vided by the type of travel: business (B), holiday (H) passenger service operations at the airport. A con- and migration (M). Thus, the set of passengers to be cept model is shown in Fig. 2. An assumption of the analysed will include of subsequent subsets of pas- model is to determine the value of the LoS index in senger groups: relation to operating scheduling of individual sta- tions and counters where individual operations are P=PPBLMBLMB ,PP ,PP ,PO ,PO ,PO ,PTR , performed and with selected passenger service oper- LMBLM (14) ation methods. The concept is that the user enters the PTR ,PTR ,PTRZ ,PTRZ ,PTRZ

Fig. 2. LoS evaluation model Kierzkowski, A., Kisiel, T., Pawlak, M., 35 Archives of Transport, 47(3),29-38, 2018

To simplify further discussion of the concept, the given elementary operation. A completed computer notation will be reduced to the symbol S that may simulation provides a set of data: assume subsequent values of integers 1,2,3…, 12. S S Whenever it is said that a passenger is included in D=〈Τi ,Γi 〉 (16) group S=1, this means passenger membership in group PPB. where S S Every subsequent passenger Pi (where the subscript ΤS= 〈τweS , … , τweS ; τc-in , … , τc-in ; … 〉 – i means the number of the subsequent passenger) i i=1 i=n i=1 i=n set of waiting times for subsequent i passengers be- will be assigned to a group of attributes (set of vari- fore all subsequent elementary operations are per- ables): formed for each passenger (noted membership in the

S S set S); P =〈S,Ψ ,Γ 〉 (15) S S i i i ΓS= 〈γweS ,…,γweS ; γc-in , … ,γc-in , … 〉 – set of i i=1 i=n i=1 i=n where: service times for subsequent i passengers for all sub- S – variable describing membership in a group; sequent elementary operations performed for each S passenger (noted membership in the set S). ΨS= 〈ψweS, ψc-in , …〉 – set of instances when the i i i passenger appears in subsequent elementary opera- In the obtained data set, it is necessary to discard tions to be performed; variables relating to operation of the elementary S S 〈 weS c-in 〉 we Γi = γ i , γ i ,… – set of service times for subse- graph G ; this is required due to the already men- quent elementary operations to be performed for the tioned fact that the graph represents an artificial op- passenger. eration that is not recognised as a queueing system and no evaluation by passengers will be made. As presented in Chapter 2, the graphs of passenger Based on the reduced data set (16), one must com- service operations make it possible to design a pute values of LoS indices for each element in the mesoscopic simulation model (Module 1, Fig. 2) data set (16) (Module 2, Fig. 2). For this purpose, the with an aim to determine individual waiting times method proposed by Anderson et al. (2008) can be and service times for each passenger in each elemen- applied. It consists of the following stages: tary operation. Since this paper continues the au- A. The survey conducted among passengers, relat- thors’ current research, the design and functioning ing to quantitative evaluation of subsequent el- of the simulation model was discussed in detail in ementary operations with simultaneous acqui- the authors’ other publications (Kierzkowski and sition of data relating to times of operation per- Kisiel, 2017a, 2017b). In the following text, the au- formance and queuing times; thors will focus on the presentation of a new model B. Estimating the regression function for variables functionality – determination of the value of the LoS τ, γ in all elementary operations; index (Module 2 and 3, Fig. 2). The presented ver- C. Determining the value of LoS indices for data sion of an expanded model assumes that the user en- set D. ters input data (flight timetable, operating schedule The method of this procedure applied during tests on of service stations and counters, methods used to a real system is presented in the publication Ander- perform elementary operations and the number of son et al. (2008). It also describes an algorithm and passengers in flights with a division into percentage a mathematical model used for statistical inference, shares in individual groups S. Based on the com- as described in section B, C. pleted tests on a real system, the simulation model Completed inference (Module 2, Fig. 2) returns a set must be equipped with the estimated functions of of data relating to individual estimated evaluations random variables suitable to determine ψ, γ and of elementary operations for each passenger, in ac- probability that the variables ξ will assume individ- cordance with the following notation: ual values. Based on the instant of appearance, ser- S S vice times of subsequent passengers and available LD=〈LΤi ,LΓi 〉 (17) service stations, the aim is to determine waiting times in the queue for all passengers that perform a 36 Kierzkowski, A., Kisiel, T., Pawlak, M., Archives of Transport, 47(3), 29-38, 2018

where: evaluation taking into account several predictors, re- S c-inS c-inS wizS lating to the determination of indices LDGS, requires LΤi = 〈lτ i=1, … , lτ i=n; … ; lτ i=1, … , i S that significance of individual elementary operations lτwiz ; … 〉 – set of evaluations of waiting times for i=n should be considered. In order to determine the val- subsequent i passengers before all subsequent ele- ues of individual weights and the fixed term, it is mentary operations are performed for each passen- possible to apply various methods as follows: AHP ger (noted membership in the set S); S (Analytic Hierarchy Process) (Saaty, 1978), ANP 퐿Γi = 〈 (Analytic Network Process) (Khan and Faisal, 2008) S S S S c-in c-in wiz wiz 〉 lγ i=1, … ,lγ i=n; … ; lγ i=1, … ,lγ i=n; … – and LS (least squares) (Kisiel et al., 2013). set of evaluations of service times for subsequent i passengers for all subsequent elementary operations 퐿표푆=〈퐿표푆푆=1, … , 퐿표푆푆=12〉 (20) performed for each passenger (noted membership in the set S). where LoS – set of global evaluations; LoSS – global evaluation of the LoS index for the Another step is to determine a set of estimated global passenger group S. evaluations of passenger service operations at the airport (Module 3, Fig. 2): 푝 S 1 S LoS = ∙ ∑ LDGk (21) S S 푝 LDG=〈LDGi=1, … ,LDGi=n〉 (18) 푘=1 where: whereLoS푆 – global evaluation of LoS for the pas- LDGS – global evaluation for the i-th passenger, with senger group S; i S memberships in the group S; LDGk – another evaluation k from the set (18), in- cluded in the group S. A universal notation used to determine the LDG in- dices can be expressed using multiple regression: The proposed method at the output of module 3 (Fig. 2) returns a set of global evaluations (20) for each m m passenger group S. The global evaluations in indi- S S (19) S S j S j vidual groups are determined based on the arithme- LDGi =w0+ ∑ wτj ∙lτ i + ∑ wγj ∙lγ i j=1 j=1 tic mean of the partial evaluations made by subse- quent passengers (21). where: S 4. Discussion LDGi – independent variable expressing the pre- dicted system evaluation given by the i-th passenger, In the presented work, a model was proposed that included in the group S, can be used as complementary to the standards spec- w0 – fixed term; ified by IATA. IATA presents a qualitative LoS rat- j – index of a subsequent operation name, where (1 ing indicator, which allows to classify a given air- – c-kin, 2 – wiz, …) port to a specific category. This model, however, is S S useful in practical process management. It allows wτj , wγ – weights assigned to evaluations of wait- j you to evaluate various scenarios for the implemen- ing time and completion time of the operation j, for tation of the process knowing the flight plan and the group S; amount of available resources. In this way, you can jS jS lτ i ,lγ i – evaluations given by the i-th passenger find the best system configuration. Analyzes can be from the group S, as regards waiting time and dura- carried out for any time periods and any boundary tion of the operation j. conditions assumed by process managers. When talking about LoS, there can be distinguish The dependent variable is the estimated evaluation other important indicators that are considered very that the passenger would give under specific condi- often (eg maximum queuing time, space in square tions of waiting and performance of subsequent ele- meters). In this model, however, they are considered mentary operations (predictors). Multi-attribute as secondary. Of course, space in square meters is Kierzkowski, A., Kisiel, T., Pawlak, M., 37 Archives of Transport, 47(3),29-38, 2018

important when designing a system. Here this sys- References tem is already built and functioning. If we have lim- [1] ANDERSON, R., CORREIAA, A.R., ited available resources, then longer waiting in WIRASINGHE, S.C. DE BARROS, A.G., check-in and shorter in security checkpoint may be 2008. Overall level of service measures for air- more beneficial or some other configurations. This port passenger terminals. Transportation Re- model looks globally at passenger satisfaction. search Part A: Policy and Practice, 42(2), 330- However, the disadvantage still remains the intro- 346. duction of input data on the stream of notifications [2] BEZERRA, G.C.L, GOMES, C.F., 2016. Meas- and service times, as well as the verification of such uring airport service quality: A multidimen- a model. sional approach. Journal of Air Transport Man- agement, 53(2016), 85-93. 5. Conclusions [3] CAPSE, 2016. Airport service evaluation re- This paper presents a new approach to the schedul- port, 2016. ing of resources required to perform passenger ser- [4] CAVES, R.E., PICKARD, C.D., 2000. The sat- vice operations at airports. The approach takes into isfaction of human needs in airport passenger account the index of level of service. The presented terminals. Transport, 147(1), 9-15. evaluation model for the system assumes that the [5] CORREIA, A.R., WIRASINGHE, S.C., 2007, user is required to enter data such as flight timetable, Development of level of service standards for passenger structure and operating schedule of the airport facilities: Application to Sao Paulo In- stations and counters dedicated to individual opera- ternational Airport. Journal of Air Transport tions. Necessary calculations require tests on a real Management, 13, 97–103. system to use the regression and multiple regression [6] CORREIA, A.R., WIRASINGHE, S.C., DE methods as well as a simulation model that has been BARROS A.G., 2008, Overall level of service described in the author’s other scientific publica- measures for airport passenger terminals. tions. Transportation Research Part A, 42, 330–346. The application of the proposed approach may result [7] DE BARROS, A.G., SOMASUNDARASWA- in direct benefits for airports given by increased RAN, A.K., WIRASINGHE S.C., 2007. Evalu- profits generated by non-aeronautical activity. Bear- ation of level of service for transfer passengers ing in mind the level of service, it is possible to fore- at airports. Journal of Air Transport Manage- cast streams of passenger relocations within the ter- ment, 13(5), 293–298. minal and adapt the working schedule of technical [8] DKMA, 2014, Four steps to a great passenger resources in individual operations so that the highest experience (without rebuilding the terminal), possible evaluation indicator of service quality can DKMA. be achieved. This will translate into waiting time in [9] HONGWEI, J., YAHUA, Z., 2016. An investi- queueing systems reduced to a minimum and in- gation of service quality, customer satisfaction creased revenues generated by non-aviation activity. and loyalty in China's market. Journal of Such an approach also makes it possible to apply dy- Air Transport Management, 57, 80-88. namic management by changing the number of [10] JACYNA-GOŁDA I., 2015. Decision-making available technical resources in the function of time model for supporting supply chain efficiency in subsequent elementary operations. evaluation. Archives of Transport, 33(1), 17-31, The proposed method assumes the use of linear re- DOI: 10.5604/08669546.1160923. gression whose suitability of application was ana- [11] JAFERNIK, H., SKLORZ, R. 2015. Initiatives lysed in scientific publications presented in refer- to reduce fuel consumption and CO2 emissions ences to this paper. Nevertheless, the next stage of during ground operations. Scientific Journal of the research will involve verification of the model Silesian University of Technology. Series based on real data and extension of the model to be Transport. 2015, 89, 47-55, DOI: able to apply fuzzy inference. 10.20858/sjsutst.2015.89.5.

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