Flight Delay Prediction using Airport Situational Awareness Map

Publication Year: 2019 Publication Type : ConferenceProceeding


The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and flight information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.


    author = {Wei Shao and Arian Prabowo and Sichen Zhao and Siyu Tan and Piotr Koniusz and Jeffrey Chan and Xinhong Hei and Bradley Feest and Flora D. Salim},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/conf/gis/0006PZTKC0FS19.bib},
    booktitle = {Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2019, Chicago, IL, USA, November 5-8, 2019},
    doi = {10.1145/3347146.3359079},
    editor = {Farnoush Banaei Kashani and Goce Trajcevski and Ralf Hartmut Güting and Lars Kulik and Shawn D. Newsam},
    pages = {432--435},
    publisher = {ACM},
    timestamp = {Fri, 27 Dec 2019 00:00:00 +0100},
    title = {Flight Delay Prediction using Airport Situational Awareness Map},
    url = {https://doi.org/10.1145/3347146.3359079},
    year = {2019}


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