Electric vehicles (EVs) eliminate tailpipe emissions and are a cornerstone of transport decarbonization; however, widespread EV adoption alone cannot overcome urban spatial capacity limits, energy peak demands, or transport inequities. Their full potential is unlocked when integrated into a diverse, shared, and interconnected mobility ecosystem.
GREENSHIFT aims to determine detailed scientific and operational requirements for deploying an optimized network of multimodal mobility hubs—consolidating public transport, shared EVs, (e-)bicycles, micromobility, and fast charging in dedicated physical spaces. Co-designed with designated Zero-Emission Zones (ZEZs) and equity-sensitive incentive policies, GREENSHIFT establishes a validated simulation-optimization framework and interactive decision-support tools for an inclusive transition toward sustainable urban mobility.
Develop an integrated simulation-optimization framework combining multi-agent behavioral modeling with physics-informed graph neural network (PIGNN) surrogates and active learning to optimize hub network design at city scale.
Analyze and optimize multi-instrument incentive packages (e.g., dynamic pricing, tradable credit schemes, gamification, and targeted subsidies) interacting with ZEZ/LEZ access regulations to steer travel behavior while protecting vulnerable populations.
Design a participatory transition methodology combining quantitative transport models with Q-methodology and open-source interactive decision-support dashboards to build institutional legitimacy and public consensus.
The project follows an incremental, cascading scientific structure coordinated from Lyon (primary continuous testbed) with international partner validation:
Lead: Dr. Bahman Madadi (ENTPE)
Coordinates project activities, operational continuity, ethics, FAIR data management (DMP OPIDoR), open-source releases, and scientific communication.
Lead: Dr. Bahman Madadi (ENTPE)
Partners: ENTPE, UGE, TU Delft
Combines dynamic agent-based multimodal choice modeling with Physics-Informed Graph Neural Network (PIGNN) surrogates and active learning for scalable network design.
Lead: Dr. Bahman Madadi (ENTPE)
Partners: ENTPE, UGE, University of Luxembourg
Investigates coordinated bundles of push (ZEZ restrictions) and pull (pricing, tradable credit schemes, gamification) instruments, ensuring mobility equity for vulnerable groups.
Lead: Dr. Bahman Madadi (ENTPE)
Partners: ENTPE, UGE, DLR (German Aerospace Center)
Bridges quantitative models with participatory Q-methodology and interactive dashboards to co-create consensus-driven transition roadmaps with stakeholders.
An open-source, scalable simulation-optimization framework combining MnMS with PIGNNs for city-scale mobility hub planning.
A validated methodology and toolset for designing and evaluating equity-constrained incentive packages interacting with ZEZs.
An open-source, interactive dashboard and validated transition roadmap enabling planners to explore progressive hub deployment scenarios.
Peer-reviewed articles in top open-access journals (CC-BY), open datasets on recherche.data.gouv.fr, and open software on GitHub.
| Institution | Key Contributors | Core Contribution |
|---|---|---|
| ENTPE / E-Mob Lab (Host) | Dr. Bahman Madadi (Coordinator/PI) Dr. Angelo Furno Dr. Christine Buisson | Project Coordination, AI/Surrogate Optimization, Stakeholder Engagement |
| Université Gustave Eiffel (UGE) | Dr. Ludovic Leclercq Dr. Nour-Eddin El Faouzi | Dynamic Traffic Modeling, Behavioral Incentives, Regional Mobility Policy |
| TU Delft (Netherlands) | Dr. Shadi Sharif Azadeh (SUM Lab) Dr. Gonçalo Correia (hEAT Lab) | Operations Research, Multimodal Hub Optimization, European Validation |
| University of Luxembourg | Prof. Francesco Viti (MobiLab) | Mobility Behavior, Decision-Support Systems, Transferability Analysis |
| German Aerospace Center (DLR) | Dr. Dimitris Milakis | Societal Acceptance, Qualitative Q-Methodology, Policy Governance |
Collaborations with regional and national stakeholders include CARA European Cluster for Mobility Solutions, CEREMA, and SYTRAL Mobilités to facilitate public participation and real-world deployment roadmaps.
📌 We are recruiting a Postdoctoral Researcher to lead Work Package 1. Rolling deadline: 15 October 2026. See the Vacancies tab for full details and how to apply.
🎉 GREENSHIFT is officially accepted for funding under the French National Research Agency (ANR) 2026 AAPG (JCJC) call under Theme H.18.
Official project launch and recruitment kickoff for Postdoctoral researchers and PhD candidates.
The transition to zero-emission urban mobility requires more than just replacing combustion engines with electric vehicles. The GREENSHIFT project aims to design strategically placed, multimodal mobility hubs that seamlessly integrate shared electric vehicles, active micromobility, and public transport. By optimizing these hubs alongside targeted behavioral incentives and zero-emission zones, we can maximize infrastructure capacity, improve accessibility, and drive a sustainable shift in travel behavior.
We are seeking a highly motivated Postdoctoral Researcher to lead Work Package 1 of the GREENSHIFT project. Your primary objective will be to develop a scalable optimization and simulation framework for multimodal mobility hub network design.
The core challenge lies in solving a highly complex bi-level optimization problem: making strategic, city-scale infrastructure decisions (hub location, mode composition, and capacity) while dynamically anticipating intricate, individual-level traveler behavior (simultaneous multimodal route and mode choices). Because coupling large-scale network optimization directly with dynamic agent-based behavioral simulators is computationally intractable, you will explore advanced, AI-driven approximation techniques to bridge this gap.
You will be working at the intersection of:
Developing and implementing AI models (specifically graph-based methods) to dramatically accelerate the evaluation of complex transport simulations.
Formulating and solving multi-objective network design and capacity allocation problems on spatial graphs.
Utilizing and adapting agent-based behavioral simulation tools (e.g., MnMS, MATSim) to realistically capture how individuals navigate multimodal networks.
Please send your application package to Bahman.MADADI at entpe.fr with the subject line [GREENSHIFT PostDoc 1 Application - Your Name]. Your application should include:
The position might be filled before the deadline, so do not wait for the last days!