Workplace: Institut Lumière Matière in Villeurbanne, CNRS and Univ. Lyon 1, near Lyon (France)
Key words: parking search; agent-based modelling; statistical physics; stochastic processes; smart navigation
We are looking for a research engineer specialised in Data Science, AI or Computational Physics for a 18-month project on the modelling of parking search. The engineer will be based at Institut Lumière Matière in Villeurbanne, next to Lyon, but will work in collaboration with a transport operator in a French metropolis and with Laboratoire 3SR in Grenoble.
Context. The cruising traffic in search of a parking space represents a significant fraction of the urban traffic (figures up to 30% have been put forward [1]), contributing to congestion and pollution in city centres. To alleviate these pains, smart parking solutions have been proposed; in particular, our transport operator partner has recently released a smart navigation application that can guide motorists towards parking spaces that are likely to be vacant, with the hope to reduce cruising.In parallel, the host team has recently developed an original modelling framework to assess the parking search time, which leverages the powerful machinery of Statistical Physics and Graph Theory [2-3].
Missions. The aim is to exploit the theoretical tools to the case of the metropolis in study, further develop them. Thus, under the guidance of the PI, the project will combine a fundamental part (namely, developing and extending a modelling framework, building on analogies with other physical systems) and an applied facet (improving the smart application and designing a rigorous tool to evaluate the environmental impact of parking policies and smart solutions). More precisely, the following steps are considered:
* applying and adapting the modelling framework to the specific case of the metropolis
* proposing theory-guided and/or data-driven improvements to the smart navigation application
* developing an objective toolbox to assess the environmental impact of parking policies
Required skills:
* Engineers' or Masters' Degree in Computer Science, Data Science or Computational Physics (or equivalent)
* Strong skills in numerical programming and / or big data manipulation
* Some knowledge of Statistical Physics and/or stochastic processes on graphs would be desirable
* Proficiency in French would be an asset
To apply, please send a CV along with a cover letter to alexandre.nicolas[at]cnrs.fr and mehdi.bouzid[at]univ-grenoble-alpes.fr
(informal enquiries are welcome!)
References
[1] Shoup, D. C. (2006). Cruising for parking. Transport policy, 13(6), 479-486.
[2] Dutta, N., Charlottin, T., & Nicolas, A. (2023). Parking search in the physical world: Calculating the search time by leveraging physical and graph theoretical methods. Transportation science.
[3] Bulckaen, L., Dutta, N., & Nicolas, A. (2022, September). Parking search in urban street networks: Taming down the complexity of the search-time problem via a coarse-graining approach. In International Conference on Parallel Processing and Applied Mathematics (pp. 470-480). Cham: Springer International Publishing.
