Prediction of Traffic Flow in Multi-Airport System

Komal K Nahar, Dipak V. Patil


A noteworthy objective of air movement executives
is to deliberately control the stream of activity with the goal
that the interest at an airplane terminal meets and does not
surpass the operational limit. In this project we are build
up an information driven structure to distinguish, portray,
and foresee movement stream designs in the terminal zone
of multi-airplane terminal frameworks toward enhanced scope
quantification choice help in complex airspace.Through this
distinguishing proof and portrayal of examples in the terminal
zone movement streams, we project intermittent usage examples
of runways, airspace and also applicable choice factors which
utilize that information to create elucidating models for metroplex
arrangement forecast and limit estimation. The system depends
on the utilization of machine learning strategies on verifiable
flight tracks, climate conjectures and air terminal operational

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Bureau of Transportation Statistics. (Aug. 2017). Airline Activity: National Summary. [Online]. Available:

J. P. B. Clarke, et al, Evaluating concepts for operations in metroplex

terminal area airspace, J. Aircraft, vol. 49, no. 3, pp. 758773, 2012.

Varun Ramanujam, et al, Estimation of maximum-Likelihood DiscreteChoice Models of the Runway Configuration Selection Process, American

Control Conference AACC, pp. 21602167, 2011.

Jacob Avery, et al, Predicting Airport Runway Configuration: A DiscreteChoice Modeling Approach, Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM), pp. 111, 2015.

Pei-Chen Barry Liu, et al, Scenario-based air traffic flow management:

From theory to practice, Elsevier, vol. 4, pp. 685702, 2008.

G. Buxi, et al, Generating probabilistic capacity profiles from weather

forecast: A design-of-experiment approach, in Proc. 9th USA/Eur. Air

Traffic Manage. Res. Develop. Seminar, Berlin, Germany, pp. 110, 2011.

C. A. Provan, et al, A probabilistic airport capacity model for improved

ground delay program planning, in Proc. IEEE/AIAA 30th Digit. Avionics

Syst. Conf., Seattle, WA, USA, pp. 2B6-12B6-12, 2011.

J. Cox, et al, Probabilistic airport acceptance rate prediction, in Proc.

AIAA Modeling Simulation Technol. Conf., San Diego, pp. 19, 2016.

E. P. Gilbo, et al, Airport capacity: Representation, estimation, optimization, IEEE Trans. Control Syst. Technol., vol. 1, no. 3, pp. 144154, 1993.

G. F. Newell, et al, Airport capacity and delays, Transp. Sci., vol. 13,

no. 3, pp. 201241, 1979.

M. Ignaccolo, et al, A simulation model for airport capacity and delay

analysis, Transp. Planning Technol., vol. 26, no. 2, pp. 135170, 2003.

L. Li, et al, A stochastic model of runway configuration planning, in

Proc. AIAA Guid, Navig, Control Conf., Toronto, ON, Canada, pp. 117,

M. J. Frankovich, et al, Optimal selection of airport runway configurations, Oper. Res., vol. 59, no. 6, pp. 14071419, 2011.

J. Avery, et al, Data-driven modeling of the airport runway configuration

selection process using maximum likelihood discrete-choice models,

M.S. Thesis, Dept. Aeronaut. Astronaut., Massachusetts Inst. Technol.,

Cambridge, MA, USA, 2016

A. D. Donaldson, et al, Improvement of terminal area capacity in

the New York airspace, M.S. Thesis, Dept. Aeronaut. Astronaut., Massachusetts Inst. Technol., Cambridge, MA, USA, 2011.

Joachim Gudmundsson, et al, Movement Patterns in Spatio-Temporal

Data, in Encyclopedia of GIS, 1st ed, S. Shekhar and H. Xiong, Eds.



[17] M. Vlachos, et al, Discovering similar multidimensional trajectories,

in Proc. 18th Int. Conf. Data Eng., Washington, DC, USA, Feb./Mar.

, pp. 673684.

Z. Fu, et al, Similarity based vehicle trajectory clustering and anomaly

detection, in Proc. 12th IEEE Int. Conf. Image Process., Genova, Italy,

Sep. 2005, pp. II-602II-605.

G. Antonini, et al, Counting pedestrians in video sequences using

trajectory clustering, IEEE Trans. Circuits Syst. Video Technol., vol. 16,

no. 8, pp. 10081020, Aug. 2016.

J.-G. Lee, et al, Trajectory clustering: A partition and group framework,

in Proc. ACM SIGMOD Conf., Beijing, 2007, pp. 593604.

S. J. Gaffney, et al, Probabilistic clustering of extratropical cyclones

using regression mixture models, Climate Dyn., vol. 29, no. 4, pp. 423440,

L. Li, et al, Anomaly detection in onboard-recorded flight data using

cluster analysis, in Proc. IEEE/AIAA 30th Digit. Avionics Syst. Conf.,

Seattle, WA, USA, pp. 4A4-14A4-11, 2011.

G. R. Sabhnani, et al, Algorithmic traffic abstraction and its application

to nextgen generic airspace, in Proc. 10th AIAA Aviation Technol.,

Integr., Oper. Conf. (ATIO), Fort Worth, TX, USA, pp. 110, 2010.

A. Eckstein, et al, Automated flight track taxonomy for measuring

benefits from performance-based navigation, in Proc. Integr. Commun.,

Navigat. Surveill. Conf., Arlington, VA, USA, pp. 112, 2009.

M. Gariel, et al, Trajectory clustering and an application to airspace monitoring, IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 15111524,


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