Optimal mixed-fleet bus scheduling and charging with direct consideration of passengers’ demand and level of service at bus stops

In the face of current environmental challenges, the shift towards more sustainable mobility is a key focus. European governments have outlined a roadmap to promote the adoption of public transportation, integrate new energy sources and reduce the reliance on single-occupancy car use, also known as “car-solo”.
This Ph.D. project focuses on optimizing a mixed bus fleet (electric and conventional) by addressing a critical question: how can we maximize the use of electric buses while ensuring optimal service quality for passengers? The goal is to reduce waiting times and enhance users’ satisfaction by accurately depicting passengers’ demand and buses travel time and arrival rates at bus stops. To achieve this, externalities such as traffic congestion and charging time must be considered.
One of the major challenges lies in balancing the ecological benefits of electric buses with the technical constraints of battery charging, particularly opportunity charging, meaning quick charging at bus stops instead of solely at depots. This approach could allow more electric buses to run, accommodate more passengers, and reduce the “bus bunching” phenomenon (where several buses arrive at once), while optimizing fleet management.
This Ph.D. thesis aims to develop optimization models for managing mixed fleets and their recharging processes. Reinforcement learning is regarded as a promising approach to leverage existing datasets about bus operations and traffic flow. Additionally, a mobility simulator (SUMO) will be used to assess the impact of the designed optimal solutions on large-scale mobility.
This research combines technological innovation, environmental sustainability, and urban planning while offering a concrete response to the mobility challenges of tomorrow.

Keywords: Sustainable mobility, traffic flow modeling, optimization, reinforcement learning

• Academic supervisor    Dr. Nadir FARHI, Université Gustave Eiffel, Cosys/Grettia, ORCID: 0000-0002-0309-8942
• Doctoral School    ED532 MSTIC, Université Gustave Eiffel
• IFPEN supervisor    Dr. Giovanni DE NUNZIO, ORCID: 0000-0003-1179-8735
• PhD location    Université Gustave Eiffel, Marne-la-Vallée, France & IFPEN, Rueil-Malmaison, France  
• Duration and start date    3 years, starting in the fourth quarter 2025 (November 3)
• Employer    IFPEN
• Academic requirements    University Master degree in Computer Science, Applied Mathematics, Operations Research, Electrical Engineering
• Language requirements    English level B2-C1. Willingness to learn French is a plus.   
• Other requirements    Good programming skills (Matlab/Python)

To apply, please send your cover letter and CV to the IFPEN supervisor indicated here below.