Sustainable Deployment Of Mobility On Demand Via Spatial Machine Learning Methods

Universities and Institutes of France
February 29, 2024
Offerd Salary:Negotiation
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Contract Type:Other
Working Time:Full time
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3 Nov 2023

Job Information


Institut Polytechnique de Paris

Research Field


Computer science » Modelling tools

Researcher Profile

First Stage Researcher (R1)



Application Deadline

29 Feb 2024 - 00:00 (Europe/Paris)

Type of Contract


Job Status


Hours Per Week


Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?


Offer Description

This is a fully funded (3 years monthly salary) PhD. Starting date can be from now to January 2024. The PhD is co-supervised by:

  • IPP , Institut Polytechnique de Paris (Palaiseau - Paris Region), excellence research and education institute (21st in Engineering in the QS World Ranking)Cl23
  • Padam Mobility (Siemens Mobility Group - Paris), enterprise that develops digital solutions for Mobility on Demand across Europe
  • INRIA (Palaiseau - Paris Region) French National Research Institute on Informatics
  • You will develop geostatistical and Machine Learning methods for planning Mobility on Demand, in order to make human mobility more sustainable, from an environmental, economic and societal point of view.


    Mobility on Demand (MoD) services are offered via a fleet of vehicles that adapt their route on the fly to the observed user requests. Today, the efficiency of MoD is measured by basic metrics, such as the number of passengers served or average delay. In this PhD we want instead to construct MoD around accessibility ,Mi19,Lo19 an indicator that measures how many opportunities (jobs, schools, shops, etc.) one can reach within a limited time, starting from a certain location. In the areas where Public Transport (PT) offers good accessibility, people do not “depend upon” their (polluting) car to participate in society. Accessibility is thus a necessary condition for social,Ma17 economicCa19,Ma17 and environmentalSa22 sustainability. Unfortunately, PT provides insufficient accessibility in the suburbs. In such areas, MoD has been shown to be more efficient than traditional PT.Ca23 We aim to exploit the potential of MoD with the objective to reduce the accessibility gap between city centres and suburbs.


    The ambition of the PhD is to answer the following research question: given a territory, which improvement of accessibility (in terms of opportunities reached within 1 hour) can we obtain via MoD? Currently, no method exists to estimate accessibility provided via MoD, based on real data. The main challenge is that accessibility is an indicator computed on a graph. While traditional PT can be modelled as a graph, MoD cannot, due to the dynamicity and stochasticity of vehicle routes.Mo16 Our novel idea consists in modelling travel times in MoD as a random field,Di23 which we can estimate via geostatistical methods and Machine Learning (ML), based on real observed trips and territory characteristics. Such estimations will allow us to build a virtual graph, on which we will compute accessibility. Our approach is multidisciplinary , as it combines Transport and Data Science, taking also into account urban planning concepts (concerning the distribution of opportunities across the territory), such as the 30 minutes city.Ne16 This PhD will shed new light on Mobility on Demand, by proposing state-of-the- art quantitative methods to support human-centred deployment of territories.


    Accessibility computations will be based on open dataOSM, GTFS, Gr, INSEE, Sirene, Po and historical real trips data from Padam Mobility. We will explore geostatistical methods such as Kriging,De18,Ya85 uncertain networks,Sa21 ML.Liu20,He18,Is21 To exploit the similarity between different territories, we will apply the concept of transfer learning , which has shown to be promising for spatial estimation.Wu21,Od17 We will use open source software (e.g., CityChrone,Lo19 qgis) as well as the development tools of Padam Mobility

    Requirements to apply

    Master 2 (or equivalent) in Engineering, Computer Science, Transport Science, Statistics or Applied Mathematics. Excellent analytical skills and programming skills. Knowledge of basic statistics. Experience with ML, Geostatistics, Geographical Information Systems is a plus.

    Interested candidates should send (i) a CV, (ii) an explanation of their previous relevant projects or skills (it can be included in the CV or as a separate document of no more than ½ page), (iii) all the marks of all the courses at BSc and MSc level, (iv) at least one recommendation letter, (v) a motivation letter. Such documents should be sent to Assoc. Prof. Andrea Araldo ([email protected]) and Louis Zigrand ([email protected]) and Dr. Aline Carneiro Viana ([email protected]).


    Ca19 Camarero, L., Oliva, J. Thinking in rural gap: mobility and social inequalities. Palgrave Commun 5, 95 (2019). https: //

    Ca23 Calabrò, G., Araldo, A. ,... & Ben-Akiva (2023). Adaptive Transit Design: Optimizing Fixed and Demand Responsive Multi-Modal Transport via Continuous Approximation. In Transportation Research Part A.

    Cl23 https: // www.

    Di23 Diepolder, S., Araldo, A. , Chouaki, T., Horl, S., Maiti, S., Antoniou, C., On modelling accessibility of flexible mobility, 11th Symposium of the European Association for Research in Transportation (hEART) 2023

    GTFS The Mobility Database.

    Gr Gridded population of the World v4. Socio Economic Data and Application Center - NASA. URL: https: //

    Ha94 Handcock, M. S., & Wallis, J. R. (1994). An approach to statistical spatial-temporal modeling of meteorological fields. Journal of the American Statistical Association(426).

    He18 Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518.

    INSEE Couples - Familles - Ménages en 2018, link

    Is21 Ismail, Hamza Y., et al. "Modelling of yields in torrefaction of olive stones using artificial intelligence coupled with kriging interpolation." Journal of Cleaner Production 326 (2021): 129020.

    Liu20 Appleby, Gabriel, Linfeng Liu, and Li-Ping Liu. "Kriging convolutional networks." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 04. 2020.

    Lo19 Biazzo, I., Monechi, B., & Loreto, V. (2019). General scores for accessibility and inequality measures in urban areas. Royal Society open science.

    Ma17 G. Mattioli. “'Forced Car Ownership' in the UK and Germany: Socio- Spatial Patterns and Potential Economic Stress Impacts”. In: Social Inclusion 5.4 (2017)

    Mi19 E. Miller. “Measuring Accessibility : Methods and Issues”. In: International Transport Forum Roundtable on Accessibility and Transport Appraisal. 2019.ù

    Mo16 Fortin, P., Morency, C., & Trépanier, M. (2016). Innovative gtfs data application for transit network analysis using a graph-oriented method. Journal of Public Transportation.

    Od17 Oda, H., Kiyohara, S., Tsuda, K., & Mizoguchi, T. (2017). Transfer learning to accelerate interface structure searches. Journal of the Physical Society of Japan , 86 (12), 123601.

    OSM Open Street Map. www.

    Po Population active occupée selon les catégories socio professionnelles des communes d'Île-de-France (données Insee), link

    Sa21 A. Saha, R. Brokkelkamp, Y. Velaj, A. Khan, and F. Bonchi, “Shortest paths and centrality in uncertain networks,” Proceedings of the VLDB Endowment, vol. 14, no. 7, pp. 1188–1201, 2021.

    Sa22 Saeidizand, P., Fransen, K., & Boussauw, K. (2022). Revisiting car dependency: A worldwide analysis of car travel in global metropolitan areas. Cities , 120 , 103467.

    Sirene Base Sirene,

    Wu21 Wu, Y., Zhuang, D., Labbe, A., & Sun, L. (2021, May). Inductive graph neural networks for spatiotemporal kriging. In Proceedings of the AAAI Conference on Artificial Intelligence

    Ya85 Theorem 2.3 de “Yakowitz, S. J., & Szidarovsky, F. (1985). A comparison of kriging with nonparametric regression methods. Journal of Multivariate Analysis.”


    Research Field


    Education Level

    Master Degree or equivalent
    Additional Information Work Location(s)

    Number of offers available



    Institut Polytechnique de Paris





    Postal Code



    19 place Marguerite Perey


    Where to apply


    [email protected]





    https: // www.


    19 place Marguerite Perey

    Postal Code



    [email protected]


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