The course is compulsory for the students in the Transportation Planning and Engineering cycle of the School of Civil Engineering. This course covers the fundamental concepts of Traffic Flow, Traffic Capacity, and Level of Service for road networks. It explores statistical theories and sampling methods relevant to traffic measurements, providing a solid foundation in data collection and analysis techniques. Additionally, the course includes hands-on experience in conducting traffic measurements and analyzing saturation phenomena, equipping students with practical skills for evaluating and optimizing roadway performance.

The course is compulsory for the students in the Transportation Planning and Engineering cycle of the School of Civil Engineering. This course covers the design of urban road systems and in particular signaling, signage and parking spaces. It includes 3 Projects on parking and signaling in an urban area and practical examples that allow students to apply theoretical knowledge to real-world scenarios.

The course is compulsory by choice for the students in the Transportation Planning and Engineering cycle of the School of Civil Engineering. The course introduces advanced concepts for the development of traffic flow models in the context of intelligent traffic control and management systems. The course includes topics such as computer-based traffic control systems; analytical traffic models; traffic simulation models and their applications; queuing theory models; machine learning models for traffic flow analysis and short-term traffic forecasting.

The course is compulsory by choice for the students in the Transportation Planning and Engineering cycle of the School of Civil Engineering. The course covers advanced concepts on quantitative methods applied in the analysis of transportation systems. The teaching expands on topics such as Intelligent Transportation Systems, network optimization and real-time traffic management and telematics systems. Applied statistical modelling (regression, classification, etc.), machine learning models for precise predictive analytics and stated and revealed preference surveys are some of the core topics of this course.

The course is optional for the students in the Transportation Planning and Engineering cycle, and other cycles of the School of Civil Engineering. The aim of the Integrated Transport Planning Topic is for the students to use real data and perform a complete analysis of the existing transport situation and the technical-economic study of a comprehensive transport development plan for a tourist island in the Aegean Sea. The Integrated Project consists of three modules: i. Transportation, ii. Traffic engineering and iii. Road construction.

The course is optional for students of the Interdepartmental Program of Postgraduate Studies (Master’s Degree Program) in the scientific field of “Data Science and Machine Learning”. The course includes the following topics: Introduction to the theory of Stochastic Processes, Series expansion of stochastic processes (Karhunen-Loeve expansion, Spectral Representation, Polynomial Chaos series expansion), Quantification of uncertainty with the Monte Carlo method, Surrogate modeling techniques, Reduced order modeling in reduced parametric spaces, neural network architectures (Feedforward neural networks, Convolutional neural networks, Autoencoders etc.), Bayesian analysis methods, Sensor data (types, spatial and temporal coverage). Applications to engineering problems and information retrieval – Correlation structures. Fourier analysis and Principal Component analysis. Data categorization. Data analysis from fixed sensors. Analysis of data from moving sensors. Data processing from mobile phone sensors (smartphone orientation, data cleaning, filtering, fusion, dimensionality reduction, feature engineering.