Airport Passenger Arrival Process: Estimation of Earliness Arrival Functions

Postorino, Maria Nadia; Mantecchini, Luca; Malandri, Caterina; Paganelli, Filippo

doi:10.1016/j.trpro.2018.12.201

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…From the passengers' perspective, check-in and security controls are key points which may be considered as the bottleneck of every airport terminal system. The number of working servers in each phase should guarantee minimum processing times as well as minimum queue length with direct impact on related costs [7].…”

Section: Introductionmentioning

confidence: 99%

The Multiphase Queuing System of the Rijeka Airport

Hess

Grbčić

2019

Pomorstvo (Online)

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The paper gives an overview of the real system as a multiphase single server queuing problem, which is a rare case in papers dealing with the application of the queueing theory. The methodological and scientific contribution of this paper is primarily in setting up the model of the real problem applying the multiphase queueing theory. The research of service system at Rijeka Airport may allow the airport to be more competitive by increasing service quality. The existing performance measures have been evaluated in order to improve Rijeka Airport queueing system, as a record number of passengers is to be expected in the next few years. Performance indicators have pointed out how the system handles congestion. The research is also focused on defining potential bottlenecks and comparing the results with IATA guidelines in terms of maximum waiting times.

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Section: Introductionmentioning

confidence: 99%

The Multiphase Queuing System of the Rijeka Airport

Hess

Grbčić

2019

Pomorstvo (Online)

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“…Based on the obtained results, the manager can prepare detailed plans of the areas to be modified or expanded. In this case study, for example, the passenger security screening and passport control in the departure area and immigration facilities in the arrival area need detailed projects that can be supported by more sophisticated method such as the simulation model [25][26][27][28][29][30][31][32][33][34][35].…”

Section: Discussionmentioning

confidence: 99%

“…); Tturn is the time turnaround (in minutes); and Cfg is a generic, conservative correction factor that takes into account delays, inconveniences, etc. Given the aircraft load factor (LF), Equations ( 31) and (32) give the area for seating passengers (AS) and area for standing passengers (ASt), and the total area A5 for waiting rooms is given by Equation ( 33):…”

Section: Spreadsheet N5-boarding Gatesmentioning

confidence: 99%

“…Indeed, they can analyze passenger flow and queuing processes within airport terminals, allowing them to reproduce the operations of actual processes including implicit variables such as human behavior and attitudes [31]. Simulation approaches have been used to model every subsystem in the airport terminal and many studies can be found in the literature [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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Airport Landside Sustainable Capacity and Level of Service of Terminal Functional Subsystems

2020

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The airport terminal is a complex building composed of many functional subsystems dimensioned to host passenger traffic, considering an appropriate level of service (LoS). The most widely known method to assess the LoS and design the terminal areas is the Airport Development Reference Manual by the International Air Transport Association (IATA). Based on this, a calculation tool in Microsoft Excel® was implemented to assess the capacity and dimension of the facilities for each functional subsystem of the terminal. The tool, composed of nine correlated spreadsheets, is a useful model to design new structures, evaluate the LoS currently offered, plan interventions, and face the new rules of passenger distancing due to the COVID-19 emergency. An international airport terminal with 20 million/year including Schengen and extra-Schengen passengers was studied. The LoS of the terminal subsystems was assessed and the areas needed for each subsystem were calculated. In the analyzed case study, most subsystems (departure hall, check-in, boarding gates, baggage claim, and arrival halls) were over-designed, according to the definition of the IATA LoS. This means that available spaces for queues and holding are sufficiently large to easily face the new rules of social distancing for passengers due to the recent COVID-19 emergency.

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“…It is evident that the most important variable is the earliness; without it, results are comparable with the random benchmark; on the contrary, the removal of any other variable produces results only marginally worse than the base model. Indeed, passenger earliness is recognized as a key factor for airport planning and management, and methodologies to estimate arrival rate functions have been recently developed [36].…”

Section: ) Fnnmentioning

confidence: 99%

Neural networks trained with WiFi traces to predict airport passenger behavior

Orsini

Gastaldi

Mantecchini

et al. 2019

2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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The use of neural networks to predict airport passenger activity choices inside the terminal is presented in this paper. Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two. Inputs to these models are both static (passenger and trip characteristics) and dynamic (real-time passenger tracking). A real-world case study exemplifies the application of these models, using anonymous WiFi traces collected at Bologna Airport to train the networks. The performance of the models were evaluated according to the misclassification rate of passenger activity choices. In the LSTM approach, two different multi-step forecasting strategies are tested. According to our findings, the direct LSTM approach provides better results than the FNN, especially when the prediction horizon is relatively short (20 minutes or less).

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Airport Passenger Arrival Process: Estimation of Earliness Arrival Functions

Cited by 19 publications

References 12 publications

The Multiphase Queuing System of the Rijeka Airport

The Multiphase Queuing System of the Rijeka Airport

Airport Landside Sustainable Capacity and Level of Service of Terminal Functional Subsystems

Neural networks trained with WiFi traces to predict airport passenger behavior

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