An operations research model for the evaluation of an airport terminal: SLAM (simple landside aggregate model)

Brunetta, Lorenzo; Righi, Luca; Andreatta, Giovanni

doi:10.1016/s0969-6997(99)00010-1

Cited by 54 publications

(25 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to Brunetta (1999) design peak-hour passenger is usually defined from historical data, and it may be the 30th busiest time of the year. Ashford (1997) has outlined below the three most important methodologies for determining design peak-hour passenger: The Standard Busy Rate (SBR), Busy Hour Rate (BHR) and Typical Peak-Hour Passenger (TPHP).…”

Section: Issn 2238-1031mentioning

confidence: 99%

Peak hour evaluation - a methodology based on Brazilian airports

Filho¹,

Müller

Wang³

2016

J. Transp. Lit.

View full text Add to dashboard Cite

Article Info AbstractKeywords: peak-hour passenger Brazilian airports clusters multiple regressionThis paper aims to establish a new methodology to calculate the design peak-hour passenger based on Brazilian Airport data. First, a cluster analysis using the Ward Hierarchical Method is applied grouping similar airports in terms of annual passenger throughput and EPH (Equivalent Peak-Hour). Then, we proceed with the calculation of the coefficients of variation of the aggregated hourly passenger throughput of the last seven years of the airports in each cluster. We propose the peak-hour for each airport cluster to be determined at the point where the stability of these coefficients is reached. We conclude by estimating a relationship of our proposed design peak-hour passenger as a function of the variables used to determine the clusters.

show abstract

Section: Issn 2238-1031mentioning

confidence: 99%

Peak hour evaluation - a methodology based on Brazilian airports

Filho¹,

Müller

Wang³

2016

J. Transp. Lit.

View full text Add to dashboard Cite

show abstract

“…Following the annual growth in aviation travel demand in the world [7 -9], longer queues due to congested travellers waiting for check-in services at airports are anticipated [10,11,9]; this calls for efficient service management strategies that would ensure better Level of Service (LOS) for check-in process at airports [12 -14]. LOS at the airport is the satisfaction derived from service facilities or operations by travellers [15]. Queues are characterised by available service units, service pattern which could be in series or parallel and queue discipline which could be; First-In-First-Out (FIFO) meaning who comes in first leaves earlier, LastIn-First-Out (LIFO) meaning, who comes in later leaves earlier.…”

Section: Introductionmentioning

confidence: 99%

Modelling of queuing process at airport check-in system: a case study of Manchester and Leeds-Bradford airports

Adeke

2018

Nig. J. Tech.

View full text Add to dashboard Cite

This study built a Simulation Model (SM) using SimEvents toolbox in MATLAB for implementing Analytical Models (AM) of queuing process at airport check-in system. Air travel demand data for Manchester and Leeds-Bradford airports in 2014 were adopted for validation of the model. There was no statistical difference between utilisation factor (UF) and service times of AM and SM outputs. Differences in AM and SM outputs for average queue length, average waiting time on queue and average number of arrivals and throughputs were attributed to variations in discrete time events considered by SM in contrary to the AM which assumed constant values for the process. The SM exhibited stochastic behaviour which actually depicts reality hence produces more reliable results. Stochastic analysis methods are therefore recommended for queuing analysis to achieve accurate results. The SM is therefore recommended to give Airport managers prior knowledge of system performance for planning and improved level of service (LOS) at airports.

show abstract

“…The original SLAM [8] did not model BHS. The new regulations on security measures in BHS, the economic impact that the consequent delays might have on airlines, the great influence that baggage flows have on airport operations and passenger movements motivated the implementation of an enhanced version of SLAM that included a model of BHS.…”

Section: Introductionmentioning

confidence: 99%