Classification algorithms for Intelligent Transport Systems

Intelligent Transport Systems (ITS) consists in the application of ICT to transport to offer new and improved services to the mobility of people and freights. While using ITS, travellers produce large quantities of data that can be collected and analysed to study their behaviour and to provide information to decision makers and planners. The thesis proposes innovative deployments of classification algorithms for Intelligent Transport System with the aim to support the decisions on traffic rerouting, bus transport demand and behaviour of two wheelers vehicles. The first part of this work provides an overview and a classification of a selection of clustering algorithms that can be implemented for the analysis of ITS data. The first contribution of this thesis is an innovative use of the agglomerative hierarchical clustering algorithm to classify similar travels in terms of their origin and destination, together with the proposal for a methodology to analyse drivers’ route choice behaviour using GPS coordinates and optimal alternatives. The clusters of repetitive travels made by a sample of drivers are then analysed to compare observed route choices to the modelled alternatives. The results of the analysis show that drivers select routes that are more reliable but that are more expensive in terms of travel time. Successively, different types of users of a service that provides information on the real time arrivals of bus at stop are classified using Support Vector Machines. The results shows that the results of the classification of different types of bus transport users can be used to update or complement the census on bus transport flows. Finally, the problem of the classification of accidents made by two wheelers vehicles is presented together with possible future application of clustering methodologies aimed at identifying and classifying the different types of accidents.

Innovative asymmetric organocatalytic processess: en route to the synthesis of biologically relevant compounds

This doctoral thesis deals with the development of novel organocatalytic strategies for asymmetric transformation. The intrinsic versatility of organocatalysis and the use of different activation modes have been exploited to achieve new catalytic enantioselective processes, towards the synthesis of biologically relevant scaffolds. The most investigated organocatalytic system have been those based on H-bond interaction (such as chiral thioureas or phosphoric acids) as well as the ones based on aminocatalysis. Despite conceptually distinct, the transformations detailed in this Thesis are linked together by simple and recurring modes of activation, induction and reactivity, promoted by the catalysts employed. The chemical diversity of the challenges encountered allows to get a precious overall view on organocatalysis, highlighting that enormous chemical diversity can be created by judicious choice of select catalyst.