Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Detection of traffic congestion and incidents from GPS trace analysis

This paper presents an expert system for detecting traffic congestion and incidents from real-time GPS data collected from GPS trackers or drivers’ smartphones. First, GPS traces are pre-processed and placed in the road map. Then, the system assigns to each road segment of the map a traffic state based on the speeds of the vehicles. Finally, it sends to the users traffic alerts based on a spatiotemporal analysis of the classified segments. Each traffic alert contains the affected area, a traffic state (e.g., incident, slowed traffic, blocked traffic), and the estimated velocity of vehicles in the area. The proposed system is intended to be a valuable support tool in traffic management for municipalities and citizens. The information produced by the system can be successfully employed to adopt actions for improving the city mobility, e.g., regulate vehicular traffic, or can be exploited by the users, who may spontaneously decide to modify their path in order to avoid the traffic jam. The elaboration performed by the expert system is independent of the context (urban o non-urban) and may be directly employed in several city road networks with almost no change of the system parameters, and without the need for a learning process or historical data. The experimental analysis was performed using a combination of simulated GPS data and real GPS data from the city of Pisa. The results on incidents show a detection rate of 91.6%, and an average detection time lower than 7 min. Regarding congestion, we show how the system is able to recognize different levels of congestion depending on different road use