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Post-doctoral research visit (F/M) in Data Sciences and Statistical Learning in Université Gustave Eiffel

24 Novembre 2022

Catégorie : Post-doctorant

Multi-source data Mining for the analysis of human mobility

Starting date: since January 2023 Main Location: Champs-Sur-Marne (77)
Duration: Up to 24 months
Level of qualifications required: PhD or equivalent
Remuneration 2600 € / month (before taxs)

This research fellow position is funded by the ANR PRCE MobiTiC project ( The post-doctoral position is offered within a research team working on statistical learning for mining complex urban data for 24 months. Several partners are involved in the project: two research laboratories of Université Gustave Eiffel (Grettia- Coordinator and Licit), the SENSE laboratory of Orange Labs, the SSP lab of Insee.

MobiTic’s methodology shall produce novel models, analyses and indicators of presence and mobility that are relevant, reliable, compliant with privacy rules, representative and frequently updated. These indicators shall be produced by combining both mobile phone network signaling and other digital and traditional data sources (smart card data, surveys, GPS data, Census data, etc.). Digital data open up fundamental perspectives for the dynamic analysis of territories, at finer levels of geographical and temporal accuracy, and may provide actors with the potential to manage their resources more efficiently, which is a requirement for sustainable development of the territories. The project will draw upon the disciplinary fields of statistics, machine learning, and data analysis.

In recent years, there has been a great deal of research work dedicated to the use of digital data for mobility analysis. A large majority of these research studies focus on the use of a single data source and a single mode of transport. In this respect, we can cite the work carried out by researchers from Université Gustave Eiffel involved in the MobiTic project on the use of smart card data to analyze the use of shared mobility systems [1], or public transport systems [2] [3], the use of Bluetooth data for road traffic analysis [4] or the use of mobile phone data to understand urban dynamics and analyze road transport networks and their resilience [5, 6, 7].

The MobiTIC project aims to combine several data sources for the analysis of human presence and mobility.

This postdoc proposal aims to develop data mining approaches to enrich mobile phone signaling data. One of the main issues to be addressed concerns the imputation of the trip mode and purpose [8, 9, 10]. Indeed, mobile phone data alone cannot provide the whole information required to build mobility indicators. In addition to socio-demographic information, indicators on human mobility (Origin-Destination matrices) can benefit from other data sources such as ticketing data, electromagnetic loop data for road traffic. The joint usage of multiple sources will make it possible to fully take advantage of the strengths of each type of source and overcome their inner individual limitations and biases. To tackle this problem, several strategies of data fusion could be investigated during this postdoc project:

The first way to tackle this problem consists to work on the scale of the individual signal trace as proposed in [11] and to incorporate within the multi-modal map-matching procedure aggregated information on the different modes provided by other data sources. Such an approach may find synergies with the work developed in [13] and also the work carried out by the LICIT laboratory [7], partner of the MobiTic project. It is also possible to combine the different data sources once the aggregation operations have been carried out [12]. Although potentially less rich, this approach simplifies processing and does not raise difficulties related to the processing of individual data.

Another interesting research direction is to detect and explore human mobility behavior in the presence of atypical situations (e.g. bad weather, accidents, extreme events, etc.) to identify and describe how people react to abnormal, sudden or unexpected situations. A better knowledge of such behaviors is of fundamental importance in the field of transport to properly calibrate the mobility offer and to implement new control solutions that can improve the resilience of the transport system. To this end, the availability of large-scale mobile phone data could be exploited and cross-referenced with other data sources (web news, social network data, historical weather data) that contain information on abnormal situations impacting mobility.

The methodologies developed will be tested and validated using available validation data.

Candidates for the post-doctoral position should already have obtained a Ph.D. in computer sciences or statistics. Other requirements are needed. In particular, candidates should have skills in distributed data mining (Spark). Strong motivation to work in an interdisciplinary project with applicative issues will be appreciated, in particular for human mobility and transport application domain. To apply, the candidate shall send a CV, a cover letter and the contacts of two referees to the contacts mentioned below.

About Université Gustave Eiffel
The candidate will be hosted in the GRETTIA laboratory (Engineering of Land Transport Networks and Advanced Computing) of the Gustave Eiffel University - Marne-la-Vallée Campus. Trips to Université Gustave Eiffel – Lyon campus are to be excpected.


Latifa Oukhellou
Research Director
14-20 Bd Newton, 77 420
Tél : +33 (0)1 81 66 87 19
Etienne Côme
Research Fellow
14-20 Bd Newton, 77 420
Tél : +33 (0)1 81 66 87 18
Angelo Furno
Research Fellow
25 Avenue François
Mitterrand, 69500 Bron
Tel. +33 (0)4 78 65 68 70

[1] E. Côme, L. Oukhellou, (2014). Model-based count series clustering for Bike-sharing system usage mining, a case study with the Vélib system of Paris, ACM Transactions on Intelligent Systems and Technology (TIST). 5(3). Ed. ACM.
[2] Briand AS, Côme E, Trépanier M, Oukhellou L (2017). Analyzing year-to-year changes in public transport passenger behaviour using smart card data, Transportation Research Part C, 79, 274-89,
[3] El Mahrsi MK, Côme E, Oukhellou L, Verleysen M (2017). Clustering Smart Card Data for Urban Mobility Analysis, IEEE Transactions on Intelligent Transportation Systems 18(3), pp. 1 - 17.
[4] P-A. Laharotte, R. Billot, E. Côme, L. Oukhellou, A. Nantes, N-E El Faouzi (2015) Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network. IEEE Transactions on Intelligent Transportation Systems 16(3): 1439-1448.
[5] Furno A, Fiore M, Stanica R, Ziemlicki C, Smoreda Z (2017). A Tale of Ten Cities: Characterizing Signatures of Mobile Traffic in Urban Areas, IEEE TMC 16(10).
[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 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).
[7] Bonnetain, L., Furno, A., Krug, J., & Faouzi, N. E. E. (2019). Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study. Transportation Research Record, 2673(7), 74-88.
[8] Bonnel, P. & Hombourger, E. & Olteanu R., A-M. & Z., Smoreda. (2015). Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations. Transportation Research Procedia. 11. 381-398. 10.1016/j.trpro.2015.12.032.
[9] Wang, H., Calabrese, F., Di Lorenzo, G., and Ratti, C. Transportation mode inference from anonymized and aggregated mobile phone call detail records. In Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on (2010), IEEE, pp. 318–323.
[10] Jiang, S. & Ferreira, J. & Gonzalez, Marta C. (2017). Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Transactions on Big Data. 3. 208 - 219. 10.1109/TBDATA.2016.2631141.
[11] F. Asgari, A. Sultan, H. Xiong, V. Gauthier and M. A. El-Yacoubi, "CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network," Computer Communications, vol. 95, pp. 69-81, 2016.
[12] Friedrich, M. & Immisch, K. & Jehlicka, P. & Otterstätter, T. & Schlaich, J. (2010). Generating Origin-Destination Matrices from Mobile Phone Trajectories. Transportation Research Record: Journal of the Transportation Research Board. 2196. 93-101. 10.3141/2196-10.
[13] D Bachir, G Khodabandelou, V Gauthier, M El Yacoubi, J Puchinger Inferring dynamic origin-destination flows by transport mode using mobile phone data, Transportation Research Part C: Emerging Technologies 101, 254-275.