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A data-driven operational model for traffic at the Dallas Fort Worth International Airport

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  • Lunacek, Monte
  • Williams, Lindy
  • Severino, Joseph
  • Ficenec, Karen
  • Ugirumurera, Juliette
  • Eash, Matthew
  • Ge, Yanbo
  • Phillips, Caleb

Abstract

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30Â minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.

Suggested Citation

  • Lunacek, Monte & Williams, Lindy & Severino, Joseph & Ficenec, Karen & Ugirumurera, Juliette & Eash, Matthew & Ge, Yanbo & Phillips, Caleb, 2021. "A data-driven operational model for traffic at the Dallas Fort Worth International Airport," Journal of Air Transport Management, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:jaitra:v:94:y:2021:i:c:s0969699721000442
    DOI: 10.1016/j.jairtraman.2021.102061
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    References listed on IDEAS

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    1. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    2. Wild, Dieter, 1997. "Short-term forecasting based on a transformation and classification of traffic volume time series," International Journal of Forecasting, Elsevier, vol. 13(1), pages 63-72, March.
    3. Tang, Jinjun & Chen, Xinqiang & Hu, Zheng & Zong, Fang & Han, Chunyang & Li, Leixiao, 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. Martin Fellendorf & Peter Vortisch, 2010. "Microscopic Traffic Flow Simulator VISSIM," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 63-93, Springer.
    5. Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    6. Aditya R. Raikwar & Rahul R. Sadawarte & Rishikesh G. More & Rutuja S. Gunjal & Parikshit N. Mahalle & Poonam N. Railkar, 2017. "Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method: A Comparability Approach with Comparable Data in Multiple Seasons," International Journal of Synthetic Emotions (IJSE), IGI Global, vol. 8(2), pages 38-50, July.
    7. Kirby, Howard R. & Watson, Susan M. & Dougherty, Mark S., 1997. "Should we use neural networks or statistical models for short-term motorway traffic forecasting?," International Journal of Forecasting, Elsevier, vol. 13(1), pages 43-50, March.
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    2. Chandra, Aitichya & Verma, Ashish & Sooraj, K.P. & Padhi, Radhakant, 2023. "Modelling and assessment of the arrival and departure process at the terminal area: A case study of Chennai international airport," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    3. Hopfe, David H. & Lee, Kiljae & Yu, Chunyan, 2024. "Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models," Journal of Air Transport Management, Elsevier, vol. 115(C).
    4. Nemanja Deretić & Dragan Stanimirović & Mohammed Al Awadh & Nikola Vujanović & Aleksandar Djukić, 2022. "SARIMA Modelling Approach for Forecasting of Traffic Accidents," Sustainability, MDPI, vol. 14(8), pages 1-18, April.

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