Simulation as a decision support tool for airport planning: Riga International Airport case study
Abstract
This research considers the aspects of decision-making according to the airport activities. The decision about airport planning and management should be comprehensive and operative and of course, the assessment of alternative decisions is necessary. The purpose of this research is to highlight the role of simulation modelling at the stage of airport development. The authors present the methodology of a model-driven decision-making approach and then describe 2 cases of using simulation for Riga International Airport (RIX) development. The 1st case study is used for analysis possibility of the development of the airport’s surrounding territory. The planned massive development of RIX and the surrounding area requires detailed analysis for increasing its positive impact on regional and national business economics, social aspects, business and the environment. The 2nd case supports decision-making for the needs of the terminal reconstruction, presents a helpful tool for visualization of the tendencies in the future, and allows the analysis of the different infrastructure layouts. Both cases give the possibility to predict the situation and evaluate the service provided for passengers (travellers) of the airport. Simulation modelling allows to study complex system – airport and evaluate direct and indirect impacts of planned reconstruction.
First published online 20 January 2022
Keyword : airport, decision, levels, models, case study, traffic volume, passenger
How to Cite
Savrasovs, M., Yatskiv (Jackiva), I., Tolujevs, J., & Jackson, I. (2021). Simulation as a decision support tool for airport planning: Riga International Airport case study. Transport, 36(6), 474-485. https://doi.org/10.3846/transport.2021.16198
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Yurshevich, E. 2013. Methodology of Decision-Making Support Based on Urban Transportation System Microscopic Models Repositories: Doctoral Thesis. Transport and Telecommunication Institute, Riga, Latvia. 204 p.
ARC. 2021. CAST Software & Solutions and Worldwide Aviation Consulting. Airport Research Center (ARC), Aachen, Germany. Available from Internet: https://arc.de
ATAC. 2018. Simmod PRO!®. ATAC Corporation, Santa Clara, CA, US. Available from Internet: https://www.atac.com/simmod-pro/
BRG. 2017. Latvia: an Essential Global Strategic Transit Hub. Brussels Research Group (BRG), Brussels, Belgium. Available from Internet: https://brusselsresearchgroup.org/thetransport-transit-and-storage-sectors-contributed-roughly-9-to-latvias-gdp/
Cascetta, E.; Carteni, A.; Pagliara, F.; Montanino, M. 2015. A new look at planning and designing transportation systems: a decision-making model based on cognitive rationality, stakeholder engagement and quantitative methods, Transport Policy 38: 27–39. https://doi.org/10.1016/j.tranpol.2014.11.005
Chen, Y.; Wu, C.-L.; Lau, P. L.; Tang, N. Y. A.; Ma N. K.; Chung, Y.-S. 2019. Airport passenger shopping modeling and simulation: targeting distance impacts, in 2019 Winter Simulation Conference (WSC), 8–11 December 2019, National Harbor, MD, US, 524–535. https://doi.org/10.1109/WSC40007.2019.9004776
CLOSER. 2012. Deliverable D4.2: Policy Advisory Group Recommendations. Connecting LOng and Short-distance networks for Efficient tRansport (CLOSER) Project. 46 p. Available from Internet: https://trimis.ec.europa.eu/sites/default/files/project/documents/20130513_124334_59698_deliverabled42.pdf
Friedrich, M.; Leurent, F.; Jackiva, I.; Fini V.; Raveau, S. 2016. From transit systems to models: purpose of modelling, in G. Gentile, K. Noekel (Eds.). Modelling Public Transport Passenger Flows in the Era of Intelligent Transport Systems. https://doi.org/10.1007/978-3-319-25082-3_4
Ginzberg, M. J.; Stohr, E. A. 1982. Decision Support Systems: Issues and Perspectives. NYU Working Paper No. IS-82-12. 42 p. Available from Internet: https://ssrn.com/abstract=1290170
Gök, Y. S.; Tomasella, M.; Guimarans, D.; Ozturk, C. 2020. A simheuristic approach for robust scheduling of airport turnaround teams, in 2020 Winter Simulation Conference (WSC), 14–18 December 2020, Orlando, FL, US, 1336–1347. https://doi.org/10.1109/WSC48552.2020.9383947
Jeppesen. 2021. Jeppesen Total Airspace and Airport Modeler®. Jeppesen – a Boeing Company, US. Available from Internet: https://ww2.jeppesen.com/airspace-solutions/total-airspaceand-airport-modeler/
Jeppesen. 2015. Total Airspace and Airport Modeller (TAAM): Product Profile. Jeppesen – a Boeing Company, US. 13 p. Available from Internet: http://ww1.jeppesen.com/documents/aviation/government/TAAM-product-profile.pdf
López, E. C.; Marmier, F.; Fontanili, F. 2019. Bus fleet size dimensioning in an international airport using discrete event simulation, in 2019 Winter Simulation Conference (WSC), 8–11 December 2019, National Harbor, MD, US, 464–475. https://doi.org/10.1109/WSC40007.2019.9004878
MoT. 2006. Transport Development Guidelines 2007–2013. Ministry of Transport (MoT) of the Republic of Latvia.
Ortúzar, J. D.; Willumsen, L. G. 2011. Modelling Transport. John Wiley & Sons, Ltd. 608 p. https://doi.org/10.1002/9781119993308
PTV. 2005. VISSIM 4.10: User Manual. PTV Planung Transport Verkehr AG, Karlsruhe, Germany. 310 p.
RIX. 2021. About RIX. Riga International Airport (RIX), Latvia. Available from Internet: https://www.riga-airport.com/aboutcompany/en
Saifutdinov, F.; Jackson, I.; Tolujevs, J.; Zmanovska, T. 2020. Digital twin as a decision support tool for airport traffic control, in 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 15–16 October 2020, Riga, Latvia, 1–5. https://doi.org/10.1109/ITMS51158.2020.9259294
San Antonio, A.; Juan, A. A.; Calvet, L.; Fonseca i Casas, P.; Guimarans, D. 2017. Using simulation to estimate critical paths and survival functions in aircraft turnaround processes, in 2017 Winter Simulation Conference (WSC), 3–6 December 2017, Las Vegas, NV, US, 3394–3403. https://doi.org/10.1109/WSC.2017.8248055
Scala, P.; Mujica, M.; Delahaye, D.; Ma, J. 2019. A generic framework for modeling airport operations at a macroscopic level, in 2019 Winter Simulation Conference (WSC), 8–11 December 2019, National Harbor, MD, US, 512–523. https://doi.org/10.1109/WSC40007.2019.9004865
Scala, P.; Mujica, M.; Wu, C.-L.; Delahaye, D. 2018. December. Sim-Opt in the loop: algorithmic framework for solving airport capacity problems, in 2018 Winter Simulation Conference (WSC), 9–12 December 2018, Gothenburg, Sweden, 2261–2272. https://doi.org/10.1109/WSC.2018.8632531
Schultz, M. 2017. Faster aircraft boarding enabled by infrastructural changes, in 2017 Winter Simulation Conference (WSC), 3–6 December 2017, Las Vegas, NV, US, 2530–2541. https://doi.org/10.1109/WSC.2017.8247981
Simio LLC. 2016. Airport Simulation Software. Simio LLC, Sewickley, PA, US. Available from Internet: https://www.simio.com/applications/airport-simulation-software/
Tomasella, M.; Clare, A.; Gök, Y. S.; Guimarans, D.; Ozturk, C. 2019. STTAR: a simheuristics-enabled scheme for multistakeholder coordination of aircraft turnaround operations, in 2019 Winter Simulation Conference (WSC), 8–11 December 2019, National Harbor, MD, US, 488–499. https://doi.org/10.1109/WSC40007.2019.9004787
Tomasella, M.; Hancock, P.; Hristova, B.; Vancova, Z.; Buke, B. 2017. Enhancing the toolkit of airport operations analysts: evidence from an airport baggage handling system improvement project, in 2017 Winter Simulation Conference (WSC), 3–6 December 2017, Las Vegas, NV, US, 2542–2553. https://doi.org/10.1109/WSC.2017.8247982
TTI. 2021. SimLab – Laboratory of Applied Modelling. Transport and Telecommunication Institute (TTI), Riga, Latvia. Available from Internet: https://tsi.lv/research/research-at-tsi/research-support-structure/simlab-laboratory-of-appliedmodelling/
Vitor, F.; Santos, V. A.; Chwif, L. 2016. Warnings about simulation revisited: improving operations in Congonhas airport, in 2016 Winter Simulation Conference (WSC), 11–14 December 2016, Washington, DC, US, 2418–2429. https://doi.org/10.1109/WSC.2016.7822281
Yatskiv (Jackiva), I.; Savrasovs, M.; Gromule, V.; Zemljanikins, V. 2016. Passenger terminal safety: simulation modelling as decision support tool, Procedia Engineering 134: 459–468. https://doi.org/10.1016/j.proeng.2016.01.068
Yurshevich, E. 2013. Methodology of Decision-Making Support Based on Urban Transportation System Microscopic Models Repositories: Doctoral Thesis. Transport and Telecommunication Institute, Riga, Latvia. 204 p.