The main areas of expertise of NTUA Dpt of Transportation Planning and Engineering with regard to ECTRI Mobility TG, include:
- Data driven analysis and forecasting of mobility characteristics using sophisticated machine learning methods and deep learning (advanced utility models, neural networks, decision trees).
- Mobility analysis and impact assessment through traffic simulation (using AIMSUN and SUMO) at macro-meso-micro level. NTUA maintains a thorough traffic model for the city of Athens which has been extensively used for analyses within the framework of research projects as well as for educational and other scientific activities.
- Collection and analysis of mobility data from various sources (manual traffic counts, public and private entities, open data platforms) to facilitate the better understanding of travel behaviour and trends, as well as provide insights to decision makers and the general public through interactive data visualisations (using Tableau).
- Large-scale acceptance and acceptability surveys and analysis for investigating users' willingness to use innovative mobility solutions (autonomous vehicles, shared services, micromobility solutions, etc.).
- Advanced mobility profiling at user level using Bayesian structures that enable the investigation of critical interelations between user characteristics and mobility needs and preferences, combined with machine learning techniques and advanced unsupervised learning methods.
- Investigation of the impacts of electromobility and alternative fuels on mobility and the road infrastructure. Identification of the socio-economic impacts of environmental transport charging policies in Athens and Greece
- Action plans for promoting electromobility and alternative fuels
- Exploitation of UAV capabilities for traffic monitoring, analysis and forecasting
- Traffic management and optimization for enhancing sustainable mobility at both user and city level by exploiting beyond state-of-the-art deep reinforcement learning algorithms.
