22 Dec 2020

    Processing geolocation location data for smart mobility

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    Type: Bachelor UROP
    Status: Available

    Human mobility modeling is widely recognized as being key to providing new services and solutions in many application domains. For example, tracking viral diseases dynamics (e.g., the COVID-19 pandemics), tracking changes in behavior which can be used to recognize impending mental health episodes, deliver more effective advertising and retail experiences, enhance security and shape the provision of urban services, etc.

    In this project, you will: (i) research the availability of labeled datasets for human mobility modeling (e.g., [1][2]); (ii) based on the research, you will select and pre-process several datasets (e.g., [1][2]); (iii) the pre-processed datasets will be visualized (e.g., using [6]) and a descriptive statistics will be presented; (iv) finally, the datasets will be compared and possible machine-learning tasks will be proposed for future work.

    References:

    1. Mokhtar, Sonia Ben, Antoine Boutet, Louafi Bouzouina, Patrick Bonnel, Olivier Brette, Lionel Brunie, Mathieu Cunche et al. “PRIVA’MOV: Analysing Human Mobility Through Multi-Sensor Datasets.” 2017 [https://hal.inria.fr/hal-01578557/document]
    2. Moro, Arielle, Vaibhav Kulkarni, Pierre-Adrien Ghiringhelli, Bertil Chapuis, and Benoit Garbinato. “Breadcrumbs: A Feature Rich Mobility Dataset with Point of Interest Annotation.” arXiv preprint arXiv:1906.12322 (2019) [https://arxiv.org/pdf/1906.12322.pdf]
    3. Luca, Massimiliano, Gianni Barlacchi, Bruno Lepri, and Luca Pappalardo. “Deep Learning for Human Mobility: a Survey on Data and Models.” arXiv preprint arXiv:2012.02825 (2020). [https://arxiv.org/pdf/2012.02825.pdf]
    4. Feng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. “Learning to Simulate Human Mobility.” In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3426-3433. 2020.
      [https://www.youtube.com/watch?v=sj4UCW0P6Ks&ab_channel=AssociationforComputingMachinery%28ACM%29]
    5. Feng, Jie, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. “Deepmove: Predicting human mobility with attentional recurrent networks.” In Proceedings of the 2018 world wide web conference, pp. 1459-1468. 2018. [https://github.com/vonfeng/DeepMove]
    6. Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. “Seqgan: Sequence generative adversarial nets with policy gradient.” In Thirty-first AAAI conference on artificial intelligence. 2017. [https://github.com/LantaoYu/SeqGAN]

     

     

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