Dimitris Nikolaou has successfully defended his PhD dissertation titled: Machine learning-based road crash risk assessment fusing infrastructure, traffic and driver behaviour data, under the supervision of NTUA Prof. George Yannis. Two distinct databases were developed; the former concerned motorway segments and the latter concerned urban and interurban road segments. Several statistical models (e.g. Logistic and Negative Binomial Regression, Hierarchical Clustering, Spatial Error Model) and Machine Learning Algorithms (e.g. Decision Tree, Random Forest, K-Nearest Neighbour and Support Vector Machine) were implemented. The results revealed that crash frequency on motorway segments is positively correlated with the traffic volume, the segment length, the number of harsh accelerations and the number of harsh brakings per segment trips. It was also concluded that geometrical and behavioural parameters can be combined to meaningfully conduct road safety analysis spatially and proactively, as they are highly correlated with harsh braking Surrogate Safety Measures.