sessions_2025

prochaine session 6 juin 2025

15h00  

Xavier Boulet, Expert mobility engineer at AIMSUN

Digital Twin & Modelling to improve the Operational Performance of Mobilities

 
 
 		 
Abstract: The presentation is dedicated to digital twins and their role in road traffic modeling. We will explore how these digital replicas enable the simulation, analysis, and optimization of traffic flow by integrating real-time data. Thanks to technological advances, we are now able to virtually recreate entire road networks, anticipate congestion, and propose dynamic solutions to improve traffic fluidity. These intelligent models provide urban managers with powerful tools to make informed decisions, reduce environmental impact, and enhance road safety
 
 
 
 		 
Bio: 
 
Dr Xavier Boulet,  holds an engineering degree and a PhD in mobility simulations. He began his professional career as a Research & Development engineer at Sopra Steria, where he worked for one year. He then spent three years at the Institute for Technological Research (IRT) SystemX, contributing to multimodal mobility simulation projects. During this time, he was involved in the development of mobility simulators and authored several research papers on multimodal traffic modeling as part of his PhD.He has now been working for five years at Aimsun SARL as a technical expert, focusing on digital twins and travel demand modeling. Email: xavier.boulet@aimsun.com
 
Aimsun provides digital solutions for smart, sustainable mobility across all modes of transport. The team designs and creates digital twins dedicated to road traffic management, planning and real time operation. The Aimsun portfolio supports clients' decision-making process for urban mobility transformation projects and improves traffic operations with real-time traffic forecasting and simulation. Aimsun provides services to around 100 clients in France, such as cities, government bodies, local communities, highway operators, airport authorities, public transport authorities and transport companies. The Aimsun portfolio provides clients with concrete benefits for traffic management: Flow Management, Decarbonization, Infrastructure Cost Efficiency, Active Mobility and Safety. Aimsun France is headed by Benoit Vedel:  benoit.vedel@aimsun.com, phone + 33 6 32 58 75 64 or www.linkedin.com/in/benoitvedel.
 

16h00  

 Prateek Bansal,  Professor at the National University of Singapore (NUS)

 

Analytical Data Fusion Approaches to Update Mobility Patterns in Real-time.

 
 		 
Abstract: Conventional activity-based models primarily rely on household travel survey (HTS) data, which often suffers from low spatial heterogeneity due to limited sampling rates. Passively collected mobility (PCM) data, such as cellular traces and transit smart card, offers extensive spatial coverage but poses significant challenges for integration with HTS data because of differences in spatial resolution and attributes. To address these limitations, this study introduces a novel data fusion approach that combines the strengths of both data sources. The proposed method focuses on two key components: first, the generation of multimodal time-dependent origin-destination (OD) matrices by integrating HTS and PCM data to improve the representation of multimodal travel demand; second, the development of complete activity schedules for synthetic populations by fusing the OD matrices generated in the first step with HTS data. With the integration of PCM data, this approach enables the estimation and real-time updating of multimodal OD demand matrices and activity schedules, paving the way for more dynamic, data-driven, and resilient transportation planning.
 
 
Bio: 
 
 Dr Prateek Bansal is a Presidential Young (Assistant) Professor at the National University of Singapore (NUS). Before joining NUS in 2022, he was a Leverhulme Trust Early Career Fellow at Imperial College London and did a Ph.D. from Cornell, an MSc from UT Austin, a BTech from IIT Delhi. Prateek leads the Behavioural Cognitive Science Lab at NUS and is a co-principal investigator of the Adaptive Mobility module at Future Cities Laboratory, Singapore. His research group is interested in creating new methods to address challenging questions related to mobility behavior and the adoption of emerging technologies at an individual level and an urban scale. His research has led to over 70 journal articles. Apart from top Transportation journals, he regularly publishes in interdisciplinary journals like Energy Economics and Statistics and Computing. He also serves as the editorial board member of Transportation Research Part A: Policy and Practice, Transportation Research Part B: Methodological, and Journal of Choice Modelling, among others. He is a member of the TRB’s standing committees on Travel Survey Methods (AEP25) and Travel Forecasting (AEP50), and a regular board member of the International Association of Travel Behavior Research (IATBR). 
 
 

19 Mars 2025

14h00  

Carlos Lima Azevedo, Professor at the Department of Technology, Management and Economics of the Technical University of Denmark and Research Affiliate of the ITS Lab at the Massachusetts Institute of Technology (MIT).

 

Individual sensing and modelling of activity, mobility and emotional patterns.

 Abstract: Emotions are an outcome of experiences, including travel, and they play a crucial role in decision-making, extending beyond classical rational choice theory. However, measuring emotions is challenging due to their latent nature, and assessing their causes and impacts on decision-making remains a complex task. The interplay between environmental stimuli and individual internal processes from a neuropsychological perspective is particularly difficult to uncover. In this seminar, I will discuss recent efforts in sensing and modeling to illuminate these connections within the urban mobility context, bridging concepts from choice modeling, machine learning, and neuroscience.
 

Short Bio:Carlos is an Associate Professor at the Department of Technology, Management and Economics of the Technical University of Denmark and a Research Affiliate of the ITS Lab at the Massachusetts Institute of Technology (MIT). He is a member of the Intelligent Transportation Systems Section and the Head of Studies for the M.Sc. in Transport and Logistics at DTU. Prior to joining DTU, he was a Research Scientist (2016-18) and the Executive Director of the Transportation Education Committee (2016-17) at MIT. He was a Senior Postdoctoral Associate at the Singapore-MIT Alliance for Research and Technology (2014-15) and a Research Scholar at the Portuguese National Laboratory for Civil Engineering (2005-13). He has a PhD (2014) in Transportation Systems, a MSc (2008) in Transportation Engineering and a 5-year BSc in Structural Engineering (2004) all from U. Lisbon. His research focuses on combining behavior modeling, machine learning and simulation of future mobility solutions. Previous research includes the development and application of individual behavior sensing and modeling frameworks, large-scale agent-based urban simulation and the design and evaluation of demand management and supply solutions for increased welfare and well-being.

15h00  

Negin Alisoltani, Assistant Professor at GRETTIA, University Gustave Eiffel, Paris, France.

 

Ride-Sharing for Sustainable Urban Solutions.

 Abstract: Urban transportation systems are increasingly challenged by traffic congestion, operational inefficiencies, and environmental impacts, primarily resulting from the extensive use of low-occupancy personal vehicles. A promising solution lies in shared mobility systems, particularly ride-sharing, which can optimize trip sharing and reduce traffic congestion. Dr. Alisoltani presents an assessment of the effectiveness of dynamic ride-sharing in alleviating congestion, with a focus on the concept of "shareability," which quantifies the potential for trips to be shared based on spatial and temporal alignment. The research extends to peer-to-peer (P2P) ride-sharing, incorporating dynamic pricing models and demand forecasting to optimize rider-driver matching and improve fleet management. These strategies not only reduce total travel distances and enhance trip shareability but also enable more efficient fleet rebalancing. Together, these approaches offer crucial insights for promoting sustainable and efficient urban transportation, providing practical strategies for urban planners and policymakers to design resilient and eco-friendly mobility solutions.
 

Short Bio:Negin Alisoltani is an Assistant Professor at GRETTIA, University Gustave Eiffel, Paris, France. With experience in both academia and industry, Negin has contributed to projects designing and simulating shared taxi and on-demand services for cities like Paris, Lyon, Rome, New York, Chicago, Singapore, and Abu Dhabi. She is involved in French national projects such as CityFab Dunkerque and ANR FORBAC projects. Her primary research interests lie at the intersection of data science, operations research, and sustainability. She is particularly focused on using clustering techniques for shared mobility services, applying deep learning for demand forecasting in both transportation and the energy sectors, and employing data science methods to address sustainability challenges.

 

30 Janvier 2025

14h00  

S. M. Hassan Mahdavi - DEPARTMENT OF HUMAN FACTORS & ECONOMICS OF SUSTAINABLE MOBILITY- Institute VEDECOM, allée des Marronniers –78000 Versailles, France.

 

Toward Viable Business Ecosystems for Integrated and Seamless Shared Urban Mobility: Insights from the EU Horizon Project "SUM" 

 Abstract:

The EU Horizon project SUM (Seamless Shared Urban Mobility) seeks to enhance the competitiveness of shared mobility and increase its modal share by addressing barriers faced by car-dependent individuals in urban areas. Through the implementation of innovative push-pull measures in nine Living Labs across Europe, SUM focuses on intermodality, sustainability, safety, and resilience to provide affordable and reliable mobility solutions for diverse stakeholders.

As part of the impact assessment of these push-pull measures, SUM has developed an ecosystemic business evaluation framework. This framework examines how stakeholders collaboratively create, capture, and deliver economic, social, and environmental value. It also evaluates the interactions, governance mechanisms, and structural components required for viable, seamless shared urban mobility. This presentation highlights the initial results of the framework, including insights from expert surveys, questionnaires, and best practices, to demonstrate how these measures and collaborative arrangements could/should support the adoption of shared urban mobility solutions.

15h00  

Bishal Sharma- COSYS-ESTAS, Univ. Gustave Eiffel, Campus de Lille, 20 rue Elisée Reclus, Villeneuve d’Ascq, F-59650, France

 

Train Rerouting and Rescheduling in Case of Perturbation: focus on Passenger Connections.  

 

Abstract: The real-time Railway Traffic Management Problem (rtRTMP) involves re-ordering and re-routing trains in case of perturbation [1]. In stations, trains can typically stop at various platforms. The platforms used by connecting trains may impact the minimum connection time necessary for passenger transfer: walking time to move between trains is shorter if they stop at adjacent platforms than at far away platforms. Indeed, the arrival of the feeder train must precede the departure of the departing one by at least the minimum connection time. A delay of the feeder train may hence propagate to the departing one unless a sufficient buffer is present. In this study, we extend the Mixed-Integer Linear Programming formulation of the state-of-the-art RECIFE-MILP algorithm for the rtRTMP [2]: we improve the modeling of connections at stations. RECIPE-MILP models railway infrastructures microscopically and allows solving instances considering all possible rerouting options. In the original RECIFE-MILP, a minimum connection time is allocated for each connection. To ensure feasibility, this time must be sufficient to allow passengers to transfer between the two farthest platforms the trains can use. We propose two enhancements to RECIFE-MILP to reduce the minimum connection time while still ensuring feasibility. The first one involves setting a short minimum connection time. We then restrict the set of platforms accessible to the trains to the pairs that are close to one another: these are the ones for which the set minimum connection time is enough for the transfer. The second enhancement considers minimum connection time to be dependent on the pair of platforms used by the connecting trains. We compare the relative performance of the two proposed enhancements with the original RECIFE-MILP considering traffic at the Lille-Flandres station, in France. We observe that both enhancements are superior to the original algorithm, with the second enhancement being even more so.