The research of COMBUSTYON focuses on real time public transport control strategies that are fully utilizing Advanced Public Transport Systems and technologies in Cooperative ITS (C-ITS).
The eCoBus research project aims to introduce next generation PT system: greener vehicles such as electric/hybrid buses (e-buses), high service quality and reduction of fuel/energy emissions.
The MERLIN aims at developing a platform to investigate the impact of different mobility solutions for the Luxembourg. More specifically, our goal is to provide a decision support tool for the government and for the info-mobility operator.
Congestion in transportation networks is reaching unprecedented levels worldwide, with increasing negative effects on economy, society and environment. Network-wide traffic control strategies have been developed by researchers in order to address this problem, but a key concept has been so far neglected: how the location and configuration of controllers influences the maximum reachable level of throughput. In this project we investigate this aspect.
As urban environments are growing fast, many major cities suffer from critical congestion levels. Even without considering externalities, such as environmental pollution, the economic loss due to traffic phenomena is immense. As a consequence, both companies and public institutions are showing an increasing interest in new collaborative mobility solutions, such as car sharing and car carpooling systems, to shape the mobility of the future.
InCoMMune is a 4-year EU Marie-Curie-funded research aimed at introducing and exploiting the concept of collaborative mobility, where users and service providers continuously collaborate by sharing information on mobility needs and by constantly adapting the services to these needs.
Workplace relocation which is an important key event in employees’ life is modifying commuting distance and thus commuting time. Using the case study of the university of Luxembourg relocating a faculty from Walferdange campus to Belval, the STABLE project aims at analyzing all kind adaptations strategies implemented by employees.
Smart mobility is explored using data collected from mobile devices. Machine learning and recommendation systems are used to develop an Intelligent Transportation System that acts as a travel assistant which provide advices for efficient usage of sharing services available.
IDEAS focuses on extending standard (Dynamic) Demand Estimation models in order to account for different activity purposes/ demand segments. The proposed framework allows estimating activity patterns using macroscopic data (traffic counts, link speeds, mobile phone data etc.).
Taking into account real time traffic conditions, the status of existing public transport services (e.g., buses, trains) and user preferences, we develop a personalised travel assistant that will proactively suggest the best transportation possibility to reach a desired destination while also balancing the load over the different transportation modes in the multimodal system.