Traffic demand forecasting and flexible planning in airport capacity expansions: Lessons from the Madrid-Barajas new terminal area master plan

Sismanidou, Athina; Tarradellas, Joan

doi:10.1016/j.cstp.2016.08.003

Cited by 13 publications

(6 citation statements)

References 53 publications

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“…An efficient transport system needs to predict its demand in order to align it to the infrastructure, supply the passengers with systems' information as well to reduce energy consumption and emissions, ensuring the maintenance of high standards of security (ANTP & World Bank, 2012). As stated by Sismanidou and Tarradellas (2018), one of the main uncertainties in investments in infrastructure projects is if the future traffic demand will be sufficient to cover the cost of investments.…”

Section: Infrastructure Projectsmentioning

confidence: 99%

Demand Forecasting model based on artificial neural networks for Passenger Transportation Projects

Vasconcelos

Quevedo-Silva

Rovai

2021

urbe, Rev. Bras. Gest. Urbana

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Whereas passenger demand is one of the main risks in passenger transport infrastructure projects on track, this paper aims to propose a demand forecasting model based on artificial neural networks (ANN) in order to contribute to the project management still in its early planning stages. For this, the design of ex post facto type was used in a descriptive research with quantitative approach, where the research group was composed by subway and train stations in the metropolitan region of São Paulo-Brazil. In total, 12 ANN were proposed architectures with 15 different configurations, totaling 180 training processes, testing and validation. For each architecture has been identified the lowest mean square error percentage obtained; and the best architecture, with a hidden layer, was performed relevance analysis by Garson method, the model 4 input variables: the population; the school enrolment; the number of jobs; and per capita income. With the proposed model, one expects to contribute to the theory by adding to demand forecasting models using a robust methodology and, for managers, serve as a tool in studies of economic and financial viability of these projects, still in its planning phase anticipated as an investment decision-making tool.

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Section: Infrastructure Projectsmentioning

confidence: 99%

Demand Forecasting model based on artificial neural networks for Passenger Transportation Projects

Vasconcelos

Quevedo-Silva

Rovai

2021

urbe, Rev. Bras. Gest. Urbana

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“…It was done by using fuzzy C-means and support vector machine. Similarly, Athina states that Madrid-Barajas planners forecasted air traffic in Madrid-Barajas airport using Spain GDP only [17]. However, it might be insufficient and can lead to unnecessary deviation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Approach towards Air Traffic Forecasting: A Case Study of Air Traffic in Changi Airport

Sailauov

Zhong

2019

Stat., optim. inf. comput.

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SouthEast Asia is considered one of the fastest air traffic growing regions in the world. Congested air traffic and weather conditions have thus become major factors in air traffic management. In this paper the model for air traffic forecasting was able to forecast the country, city-pair and airport-pair air traffic. Results show that the passenger forecasting for Singapore has dependence not only on that country but on neighbouring countries as well. The paper predicts that the passenger movements in Changi airport will increase up to 81.65 million people by year 2023, which is 31.23 % more than that in 2017. Also, the number of passenger aircraft between Singapore and Jakarta city-pair will increase up to 34702 by year 2023, which is 26.6 % more than that in 2017. In addition, the number of passenger aircraft between Changi airport and KLIA airport-pair will be between 31698 and 40311 by year 2023.

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“…Strategic design parameters of airports in Thailand set out Pandey (2020) who focused in his study on airport airside facilities, aeronautical tariff and other criteria and demonstrated the importance of fuzzy based QFD method for customeroriented airport strategic planning (Pandey 2020). The case study of the Barajas airport (Madrid, Spain) terminal area master plan carried out an ex-post evaluation of the demand forecasts of the airport's last capacity expansion in 2006 (Sismanidou, Tarradellas 2017) and the article of Tisdall et al (2020) aimed at the problem of "affirmative planning action by stakeholders seeking to replicate international successes". Nevertheless, any of these articles did not investigate optimum planning horizon.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Airport Planning: Approaches to Determining the Planning Horizon

Kazda,

Novák Sedláčková,

Bračić

2023

Transport

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Airport planning is a challenging task that requires knowledge of many standards and recommended practices, bylaws and procedures. Besides, it is possible that politicians would try to intervene in the planning process, which always exceeds the election period of one government. Therefore, the article provides in-depth theoretical analysis of the problem and summarizes the results of research that focused on comparing the approach to the airport planning issues in the Slovakia and Croatia. The primary goal was to develop a methodology for determining the airport planning horizon, to assess the significance of individual planning phases and to evaluate results. The research was carried out using a combination of several methods. The main challenge was to determine the length of the planning horizon. In 2 panels, 32 experts from Slovakia and Croatia were interviewed and 224 different responses were received and processed by the fuzzy Delphi method. The advantage of this approach relies on combination of well – developed theory and practical solutions in cooperation with experts from the industry. Despite the different legal frameworks and similar standards for airport planning in both countries, the results of the research proved that the values of the optimal planning horizons are comparable. As a result, the methodology can therefore be used in other countries with similar conditions. However, planning procedures and practices depend on the specifics of states or even regions. Eventually, the experience from the research provides relevant and robust material to support teaching. Besides, it is transferable to other fields of transport infrastructure planning. Additionally, the research results were provided to the state planning authorities.

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Traffic demand forecasting and flexible planning in airport capacity expansions: Lessons from the Madrid-Barajas new terminal area master plan

Cited by 13 publications

References 53 publications

Demand Forecasting model based on artificial neural networks for Passenger Transportation Projects

Demand Forecasting model based on artificial neural networks for Passenger Transportation Projects

An Optimization Approach towards Air Traffic Forecasting: A Case Study of Air Traffic in Changi Airport

Airport Planning: Approaches to Determining the Planning Horizon

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