Phd Position F/m Phd Thesis Proposal
Il y a 5 mois
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 7 research support departments.
Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE,), but also with key economic players in the area.
The Centre Inria de l’Université Grenoble Alpe is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.
**Contexte et atouts du poste**:
Motivation of PhD
Our society is more and more conscious of the contribution of current mobility modes to the climate crisis. This is why innovative low-carbon mobility solutions are being promoted by decision-makers and increasingly adopted by citizens. It is, for instance, expected that Electric Vehicles (EVs) will account for 70% of sold vehicles by 2030. The EU Commission, with its Fit 55 plan, even envisions a ban on the sale of new petrol and diesel cars as early as 2035. Meanwhile, adoption of micromobility modes is increasing significantly. Micromobility is an umbrella term used to describe the category of transportation using non-conventional battery-powered vehicles aimed at shrinking the physical and environmental footprint required for quickly moving people over relatively short distances. With micromobility, urban transportation modes have diversified very quickly. The challenge for cities encompasses organization and planning of public space and promotion of active mobility for health purpose given the passivity of some micromobility modes (e-scooters in particular). The co-existence of these modes in shared spaces cause various kinds of inconvenience for other users (people in wheelchairs, walking with a baby in a stroller, or elderly people) and alters the perception of safety which can lead vulnerable people to be more sedentary. Beyond the perception, it is attested that the number of accidents due to e-scooters is constantly increasing. It is therefore crucial to monitor the use of these micromobility modes by collecting information in a dynamic and non-intrusive way and then make recommendations for safer shared spaces and physical activity.
**Principales activités**:
Proposed work during PhD
Three main tasks are envisioned for this thesis:
a)
- City-wide mobility model: This task aims at developing a dynamic network model for multimodal mobility over a city. For this purpose, our starting point will be the recent works by the team which developed a large-scale mobility model to characterize the daily movement of people in an urban network. This model is based on the modeling of people's mobility between their place of residence and 5 categories of destinations (work, schools, etc.). It generates a graph with nodes (origins and destinations) and also their interconnections through the origin-destination matrix that characterizes: directions, weights and temporal profile of the connections between nodes. The model simulates the movement of people at an aggregate level (no distinction of individuals, no information on the routes connecting origin and destination), Pratap et al. 2022. It has been used to control epidemics propagation while preserving the territory productivity, Niazi et al. 2021.
For monitoring multi-modal mobility, we will divide the city in cells. Each cell will define a node of the mobility networks. Each node will have several states representing the number of users for each mobility mode. Transition can be done from one mode to another. Therefore, there will be a dynamic for mobility mode in each node. Each node will interact with its neighbors. Two nodes will be adjacent if there is at least one mode from which people can jump from one node to another. The graph is expected to be large and dense with weights related to mobility between nodes. The originality of this task rests in the finer grain of the proposed description and the accurate distinction between the possible transportation modes, including cars, public transportation and micromobility.
b)
- From discrete to continuous: Here we will develop a dynamic continuous counterpart to the discrete city-wide network of the previous task by using graphons (Ruiz et al, 2021) and/or continuation (Nikitin et al. 2021). The city-wide network from the previous task is equipped with dynamics for the evolution of the shares of the mobility modes. We expect that these dynamics will feature diffusion and transport terms: therefore, the dynamics belo
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