Prediction of Traffic Flow in Multi-Airport System
Abstract
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
information.
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Bureau of Transportation Statistics. (Aug. 2017). Airline Activity: National Summary. [Online]. Available: https://www.transtats.bts.gov
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.
Berlin, Germany: Springer, IEEE TRANSACTIONS ON INTELLIGENT
TRANSPORTATION SYSTEMS
[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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