On 8th November 2019, a meeting on the ambitions, objectives and first results of WP2 of the PROMENADE project (Platform for Resilient Multi-modal Mobility via Multi-layer Networks & Real-time Big-Data Processing) has taken place at IFSTTAR, Bron, at the presence of different researchers of ENTPE-IFSTTAR and the other partners / collaborators of the project


  • Angelo FURNO, researcher LICIT
  • Loïc BONNETAIN, PhD student LICIT
  • Manon SEPPECHER, PhD student LICIT
  • Marco FIORE, researcher CNR Italy
  • Razvan STANICA, researcher INRIA, CITI-Lab, INSA-Lyon
  • Zbigniew SMOREDA, researcher ORANGE LABS
  • Stefania RUBRICHI, researcher ORANGE Labs

Connected via videoconference:

  • Cezary Ziemlicki, engineer ORANGE Labs
  • Chi Dung PHUNG, postdoc ORANGE Labs
  • Bruno Bouchoir, engineer ORANGE Labs
  • Lino GALIANA, researcher INSEE

The discussion, moderated by Angelo FURNO, had the objective to present and discuss the problems related to multi-source data collection and processing of mobile phone data for mobility/presence estimation.

Angelo FURNO has introduced the PROMENADE project, by presenting the general goal of the project and the platform, the main objectives of each work package and, more specifically, the tasks related to WP2.

On the same day, he has announced that the extension of the PROMENADE project from 3 to 4 years (1stFebruary 2019 to 31st Januray 2023) has been accepted by ANR.

Angelo FURNO has recalled that, during the first 9 months of the project, strongly based on the work of the PhD thesis of Loic BONNETAIN (tutored by A. FURNO and N.E. EL FAOUZI), two different main contributions (detailed next by L. BONNETAIN) have been achieved, in relation to the first task (Large-scale Mobile Phone Data Analysis for Urban Mobility Reconstruction) of the WP2 of the project (Big data approaches for mobility reconstruction).

Angelo FURNO has also presented to the partners the preliminary architecture and technological choices for the PROMENADE platform. Both the architecture and the preliminary implementation of the platform are part of a contribution (De Iasio A., Furno A., et al., A Microservices Platform for Monitoring and Analysis of IoT Traffic Data in Smart Cities) that has been accepted at the Big Data 2019 conference, in 2019 International Workshop on IoT Big Data and Blockchain (IoTBB’2019).

The PROMENADE workshop has continued with an intervention from M. SEPPECHER on Zonal-speed estimation via mobile phone data. Manon SEPPECHER is a LICIT PhD student, co-tutored by L. Leclercq, D. Lejri and A. FURNO and CITEPA. She is working on developing a methodology aiming to reconstruct estimation of traffic indicators (i.e., dynamic speed information) for a given zoning of the city of Lyon (reservoirs) using CDR data.

The discussion has continued with an intervention (remotely) from Orange Labs on the latest data collection that has been performed in the framework of the ANR CANCAN project, involving, besides Orange, colleagues from INRIA (R. STANICA) and CNR-Italy (M. FIORE), both present at the meeting.

ZIEMLICKI and D. PHUNG have illustrated preliminary time series related to the volume of collected data and widely discussed a few problems related to the current probes used by Orange. The amount of data collected daily is enormous for the whole metropolitan France (~ 1.2TB / day).

FIORE has then discussed his current research on mobile phone data. The presentation has presented three main topics: population density estimation, sparse trajectory completion and geography of mobile service usage.

The following intervention from L. GALIANA from INSEE has focused on the questions related to spatial segregation. The INSEE researcher has used Orange 2007 individual CDR data to go beyond the analysis of tax income data for residential segregation conclusions. The researcher is exploring instead the questions related to how mobility affects urban segregation, by combining phone and traditional tax income data.

After the lunch break, Z. SMOREDA has presented the most recent work he has conducted in collaboration with the PhD student M. Vanhoof on home detection via mobile phone data. Five different techniques have been analyzed from the literature to infer home locations and population estimates from CDR data.

B. BOUCHOIR has then described an experience-based set of heuristics developed to detect user stagnations, i.e., phases in which a user is immobile from 4G mobile phone data.

S. RUBRICHI has instead presented her study on epidemics spreading via models and metrics from complex network theory, computed using mobile phone data to quantify users’ mobility and presence in specific regions of Africa.

Finally, Loic BONNETAIN has discussed his contribution to the PROMENADE project in his thesis activity. Specifically, in a first paper, published at the TRR journal (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, 0361198119847472), an approach for the reconstruction of multimodal itineraries from mobile phone data – based on a Hidden Markov Model technique – has been developed and successfully tested.

Secondly, in a paper recently accepted at the TRB 2020 conference (Fekih, M., Bellemans T., Furno, A., Bonnetain L., et al. (2020). Assessing The Potential Of Cellular Signaling Data To Generate Dynamic Travel Patterns: A Comparative Study With Travel Survey Data. Transportation Research Board, 2020), a large-scale dataset of 2G and 3G mobile phone network signaling data has been studied and leveraged to develop an approach for reconstruction of dynamic OD matrix and for the analysis of OD spatio-temporal patterns.

You can download here the presentation on PROMENADE and WP2 by A. FURNO.


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