CHEN Longbiao

PhD graduated
Team : Phare
Departure date : 12/31/2018

Supervision : Thi-Mai-Trang NGUYEN

Co-supervision : PAN Gang, JAKUBOWICZ Jérémie

Big Data-Driven Optimization in Transportation and Communication Networks

The evolution of metropolitan structures and the development of urban systems have created various kinds of urban networks, among which two types of networks are of great importance for our daily life, the transportation networks corresponding to human mobility in the physical space, and the communication networks supporting human interactions in the digital space. The rapid expansion in the scope and scale of these two networks raises a series of fundamental research questions on how to optimize these networks for their users. Some of the major objectives include demand responsiveness, anomaly awareness, cost effectiveness, energy efficiency, and service quality. To achieve the optimization objectives, various analytical models, optimization algorithms, and simulation systems have been proposed and extensively studied across multiple disciplines. Generally, these simulation-based models are not evaluated in real-world networks, which may lead to sub-optimal results in deployment.
With the emergence of ubiquitous sensing, communication and computing diagrams, a massive number of urban network data can be collected. Recent advances in big data analytics techniques have provided researchers great potentials to understand these data. Motivated by this trend, we aim to explore a new big data-driven network optimization paradigm, in which we address the above-mentioned research challenges by applying state-of-the-art data analytics methods to achieve network optimization goals. First, by analyzing large-scale traffic datasets from both networks, we propose a graph-based traffic clustering algorithm to better understand the traffic similarities and variations across different area and time. Upon this basis, we apply the traffic clustering algorithm to the following two network optimization applications. Second, by analyzing large-scale user mobility datasets from both networks, we propose a spatio-temporal mobility prediction algorithm to better model the mobility patterns and fluctuations across different area and time. Based upon this, we apply the mobility prediction algorithm to the following two network optimization applications. Finally, we summarize the insights learned from the big data-driven network optimization paradigms, and discusses the future research directions.

Defence : 07/05/2018

Jury members :

Anelise Munaretto FONSECA, Professeur, Universidade Technológica Federal do Paraná [Rapporteur]
Marco FIORE, Chargé de recherche, HDR, CNR-IEIIT [Rapporteur]
Guy PUJOLLE, Professeur, Sorbonne Université
Igor Monteiro MORAES, Professeur, Universidade Federal Fluminense
Shijian LI, Professeur, Zhejiang Université
Thi-Mai-Trang NGUYEN, Maître de Conférences, Sorbonne Université
Gang PAN, Professeur, Zhejiang Université
Jérémie JAKUBOWICZ, Maître de conférences, Télécom SudParis

Departure date : 12/31/2018

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