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Deadline to apply: 30th May 2021
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In the last decade, the multi-modal transportation system of large cities has been profoundly jeopardized by a variety of sudden and extreme perturbations . According to the World Economic Forum’s Global Risks Report 2019, extreme weather events are among the global risks of highest concern. Heavy precipitation, along with associated flooding in urban mega-regions, has been on the rise both in intensity and frequency under the dual forcings of climate change and rapid urbanization . Similarly, in recent times, the COVID-19 pandemic has radically transformed human mobility habits, leading to globally unprecedented decline in transit ridership as well as drastic reduction of capacity of transit as a consequence of social distancing .
These factors of vulnerability related to transport are exacerbated by the fact that a transportation network is a complex entity composed of multiple interdependent subsystems (underground, train, tramway, bus transit, and road network), which are spatially constrained and that also rely on other urban infrastructure systems such as the power grid and communication networks. Thus, even limited disruptions in one component of this complex system, often triggered by exogenous hardly predictable events, can lead to a severe loss of lifeline functions via cascading failures. Furthermore, as urban transport systems are becoming increasingly connected and autonomous, one should also consider the growing threat of opportunistically targeted cyber-attacks designed to take advantage of natural hazard events .
In this context, this thesis proposes to investigate approaches based on complex network theory and network optimization towards: i) advancing the study of the resilience of multi-modal urban transport systems by means of an advanced multi-layer modelling of the urban transport network; ii) defining a tool to support the design of complex disruptive scenarios, coupling targeted attacks, weather-related phenomena as well as sudden variations of the demand and offer of the transport system induced by exogenous factors (floods, pandemic, etc.); iii) evaluating their impacts on the performance of the existing transit system in terms of complex networks metrics.
The thesis will also explore solutions for resilience enhancement based on (topological) reconfiguration scenarios via network optimization and integration of on-demand mobility facilities (e.g., park-and-ride) in order to support the dynamic adaptation of the system to such variations and rapid recovery from extreme perturbations with increased resilience.
The subject is at the interface between network science and transportation modelling, with possible applications in the field of operations research.
The thesis program will develop around the following scientific challenges:
The Phd student will have to advance current methodologies developed within the team on resilience analysis and optimisation of complex transportation networks.
She/he will have to develop as well novel and efficient approaches based on multilayer networks, resilience metrics and perturbation scenario design towards characterisation of the resilience of the multimodal transport system.
It is expected that the successful candidate will contribute to top-tier network science and transportation conferences and journals (Transportation Research Board, IEEE Intelligent Transportation Systems, Transportation Research Part B, C, E, NetSci, Networks, Applied Network Science, IEEE Transactions on Network Science and Engineering, etc.).
Lyon - Auvergne-Rhône-Alpes - France
Mobilité géographique : Européenne
The Transport and Traffic Engineering Laboratory (LICIT) is a Joint Research Unit under the dual administrative supervision of the French University Gustave Eiffel (UGE) and the National Post-Graduate School of Public Civil Engineering (ENTPE). It is recognized for its work on traffic modelling and engineering. The laboratory has already developed many successful applications for both traffic information and simulation tools.
Web-site : http://licit.ifsttar.fr
Master two degree in Computer Science, Civil Engineering, Physics, Mathematics and Network Science.
The phd student should have an expertise on computer and network science as well as complex systems modelling. Knowledge of traffic theory, data science and operations research tools will be considered as a plus.
Proven written and verbal communication skills with fluency in written and spoken English.
Please send an email to:
including the following elements:
 Markolf, S. A., Hoehne, C., Fraser, A., Chester, M. V., & Underwood, B. S. (2019). Transportation resilience to climate change and extreme weather events–Beyond risk and robustness. Transport policy, 74, 174-186.
 Yadav, N., Chatterjee, S., & Ganguly, A. R. (2020). Resilience of Urban transport network-of-networks under intense flood Hazards exacerbated by targeted Attacks. Scientific reports, 10(1), 1-14.
 Hu, S., & Chen, P. (2021). Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership. Transportation Research Part D: Transport and Environment, 90, 102654
 Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of complex networks, 2(3), 203-271.
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 Gu, Y., Fu, X., Liu, Z., Xu, X., & Chen, A. (2020). Performance of transportation network under perturbations: reliability, vulnerability, and resilience. Transportation Research Part E: Logistics and Transportation Review, 133, 101809.
 da Fontoura Costa, Luciano. "Reinforcing the resilience of complex networks." Physical Review E 69.6 (2004): 066127.
 Alenazi, M. J., & Sterbenz, J. P. (2015, October). Evaluation and comparison of several graph robustness metrics to improve network resilience. In 2015 7th International Workshop on Reliable Networks Design and Modeling (RNDM) (pp. 7-13). IEEE.