An emerging approach is expected to change the way of designing and assessing the future mobility of people and goods. Whether it will be more electrified, connected, automated, digitalized, or not, it will depend on which targets we aim to reach at given time horizons: environmental, economic, societal, etc. In the new paradigm, these targets will be set first, then the most appropriate roadmap of policies and technologies needed will be established (“backcasting”), as opposed to the current approach where potential actions are assumed, then their impacts are “forecasted” and assessed. Backcasting can be seen as a dynamic optimization process (optimal control). It will be enabled by the availability of a system dynamic model that describes mobility systems in very macroscopic terms yet capable of representing all causality loops linking the policies sought to the impacts expected.

This doctoral topic represents the first building block of such an ambitious research program, as it targets a system dynamic model of the transportation network subsystem. A precise definition of manipulable and exogenous input, internal dynamic states, as well as of significant output variables, shall be the result of a preliminary, System Dynamics analysis. To identify and quantify the causal dependencies between these variables, a multimodal transportation network simulator (e.g., MatSim) will be used. Simulation experiments will be designed and run. The corresponding inputs and outputs variables will be used to construct surrogate models (the most suitable technique is to be identified) and tune their parameters, to provide the quantitative dependencies sought. The analysis will focus on different scenarios, to anticipate the effects of exogenous variables. Different networks or territorial scales will be attempted as well, to investigate the variability of the transfer functions identified and their scalability.

Doctoral School: ED580 - STIC Sciences et Technologies de l’Information et de la Communication

Supervisor: Dr. Antonio SCIARRETTA, IFP Energies nouvelles, antonio.sciarretta@ifpen.fr

PhD location: IFP Energies nouvelles, Rueil-Malmaison, France

Duration and start date: 3 years, starting in fourth quarter 2023

Employer: IFP Energies nouvelles, Rueil-Malmaison, France

Academic requirements: University Master’s degree in Transportation Engineering, Information Engineering, or Artificial Inteligence

Language requirements: Fluency in English or French, willingness to learn French

To apply, please send your cover letter and CV to Dr. Antonio SCIARRETTA.