Optimization of the environmental performance of urban mobility via macroscopic and multimodal modeling approaches

Environmental and health concerns are now accelerating behavioral changes related to personal mobility in an unprecedented way.
On the one hand, we are witnessing major changes in transport demand linked to new technologies and new types of employment (spread of remote working, preference for remote meetings, etc.). On the other hand, on the supply side, the multiplication of transport modes mainly in the field of soft and/or shared mobility makes the existing road infrastructures more and more shared by vehicles of different nature (e.g. bicycles, cars, buses, motorcycles, streetcars, etc.).
Conventional approaches to modeling mobility flows, based on static demand data (e.g., mobility surveys that give a picture of demand at a specific instant in time) and that distinguish at best only a few classes of vehicles traveling on the same lanes in the same direction, are rapidly becoming obsolete. Indeed, they are neither able to follow the rapid evolution of mobility, nor to describe the complex interactions that can occur between vehicles of very different nature.
The objective of this Ph.D. project is to develop a new macroscopic model of multi-class mobility flows capable of describing the movements and interactions of the different modes of transport that we observe today in our cities. Second, we seek to understand what actions need to be taken to reduce the negative impact of mobility on air quality in our cities. In order to meet the operational needs of cities, the control actions studied in this project will be mainly modal shares and routing suggestions at intersections, which are known to be among the most effective “actuators” to improve mobility performance.

Keywords: sustainable mobility, road traffic, kinematic wave model, optimal routing, optimal modal share

• Academic supervisor    Dr. GOATIN Paola, Inria centre at Université Côte d'Azur (ACUMES team) https://orcid.org/0000-0001-5169-1751
• Doctoral School    ED364 SFA, http://www.ed-sfa-unice.fr/
• IFPEN supervisor    Dr. DE NUNZIO Giovanni, Control, Signal and System Department, giovanni.de-nunzio@ifpen.fr, https://orcid.org/0000-0003-1179-8735
• PhD location    Inria centre at Université Côte d'Azur, Sophia-Antipolis, France and IFP Energies nouvelles, Lyon, France
• Duration and start date    3 years, starting in November 2022
• Employer    IFP Energies nouvelles, Lyon, France
• Academic requirements    University Master’s degree in Mathematics or Electrical Engineering, specialization in Automation 
• Language requirements    Fluency in English. Fluency in French or willingness to learn French is a plus
• Other requirements   Good programming skills in Matlab/Python