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Dear colleagues,

We are looking for an enthusiastic student interested in a Phd on network science applied to transportation analysis.

Please, consider sharing this call among potential candidates and colleagues.

If interested to apply, please send the following documents to [log in to unmask] and [log in to unmask] :
  • A curriculum vitae;
  • Transcript of records (marks);
  • A research letter discussing a personal vision of the position, wrt references proposed in the call (see below)
  • At least, one recommendation letter and a list of references to contact.

Thesis title: 

A Complex Network-Based Framework for Resilience Characterisation and Optimisation of Large-scale Multi-modal Transport Networks Under High-Risk Failures and Perturbations

Deadline to apply: 30th May 2021

Please contact us at the following mail addresses:


In the last decade, the multi-modal transportation system of large cities has been profoundly jeopardized by a variety of sudden and extreme perturbations [1]. 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 [2]. 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 [3].

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 [2].

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.

Main activities

The thesis program will develop around the following scientific challenges:

  • Modelling and coding of the multi-modal transport network of the Lyon urban area, by focusing on its transit system (bus, tramway, underground) and the city road network. An approach based on multi-layer networks [1, 4, 5] will be leveraged by relying on data from the National Institute of Geography (IGN) and from the local provider of the transit system of Lyon (Keolis-Sytral). The augmentation of the model with travel demand information will be considered as an essential research direction, based on previous work from the team [6].
  • Identification of complex networks metrics to describe the resilience and robustness of the multi-modal transport network. In particular, the size of the giant connected component (GCC), network efficiency, adapted to the context of multi-layer modelling and cascading failures [2], will be a potential candidate for robustness quantification in dynamic configurations. Additional metrics related to vulnerability, robustness and resilience for characterizing the performance of transport systems under disturbance will be explored as well [7].
  • Definition of a framework for the injection of multiple joint failures in the multi-modal transport system (disruptive scenario testing). More traditional strategies based on random failures as well as more complex approaches involving flood probability modelling and high centrality node failures will be investigated to simulate high-risk scenarios and evaluate their impact on the aforementioned robustness metrics. The expected solutions should allow modelling of compound disruptions, including flood scenarios combined with targeted attacks as well as global reduction of the transit capacity or travel demand.
  • Analysis of optimisation strategies for improvement of network robustness. Solutions based on optimal graph augmentation [8, 9], identification of the most critical sub-network, as well as the optimal allocation of on-demand mobility facilities (e.g., park-and-ride facility location)  for increased network robustness will be investigated.

Research Objectives

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.).

Place of work

Lyon - Auvergne-Rhône-Alpes - France
Mobilité géographique : Européenne

Deadline for applying: 31/05/2021

Starting date: 01/10/2021

Details on the Employer

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 :

Expected Candidate Profile

Level of qualifications required:

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.

How to apply

Please send an email to:

including the following elements:

  • A curriculum vitae;
  • The complete record of master grades (relevé de notes M1 and M2 for French candidates)
  • A two-page-most motivation letter discussing how the candidate's background and research interests relate to the proposed subject and bibliographic references.

Bibliographic References

[1] 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.

[2] 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.

[3] 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

[4] 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.

[5] Salehi, M., Sharma, R., Marzolla, M., Magnani, M., Siyari, P., & Montesi, D. (2015). Spreading processes in multilayer networks. IEEE Transactions on Network Science and Engineering, 2(2), 65-83.

[6] Henry, E., Bonnetain, L., Furno, A., El Faouzi, N. E., & Zimeo, E. (2019, June). Spatio-temporal correlations of betweenness centrality and traffic metrics. In 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (pp. 1-10). IEEE.

[7] 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.

[8] da Fontoura Costa, Luciano. "Reinforcing the resilience of complex networks." Physical Review E 69.6 (2004): 066127.

[9] 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.

Apologies for cross-posting.
Angelo Furno
LICIT laboratory (Lyon, France),
University of Lyon & University Gustave-Eiffel,
phone: +33 (0)4 78 65 68 70
skype: angelo.furno

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