The growth of information and communication technologies (ICT) affects all areas of society. This digital transformation has a disruptive nature in mobility and transport: Intelligent Transport Systems (ITS) is intended to improve the effectiveness and efficiency of transportation systems through advanced technologies in information systems, communications, and sensors. This course considers ITS as a lens through which one can view many transportation and societal issues. The subject should be of interest to students interested in the performance, control, and management of transportation systems and the deployment of advanced technology systems. The course will introduce the fundamentals of transport modelling and ITS, focusing on design and technological aspects. Traffic flow theory and traffic flow models in micro- and macroscopic levels are studied. Network-level aggregated modeling and control approaches are introduced. Advanced traffic management schemes (such as adaptive traffic signal control) are discussed. It also discusses current practices and new methods for data collection and analysis, performance monitoring, route design, frequency determination, and vehicle and crew scheduling. Finally, the course will assess the impact of recent trends such as environmental, social, and economic sustainability, the introduction of low emission zones, Mobility as a Service (MaaS), and the future of connected and autonomous vehicles.

Knowledge and abilities: • (Knowledge) Familiarise with elements of transportation systems • (Knowledge) Understand the physics of the transport phenomena • (Knowledge) Illustrate ICT tools and methods for transport  • (Knowledge) Compare transportation and traffic models • (Knowledge) Discuss techniques for transport management and control • (Knowledge) ITS applications to public transport, to fleets management, and to the transport demand management • (Ability) Critically analyse the existing transport systems • (Ability) Mobility data collection, cleaning, and value extraction • (Ability) Apply the fundamentals of transportation engineering in real case studies • (Ability) Use advanced statistical techniques and data mining to analyse (big) data • (Ability) Design "smart" transport systems with the help of the ICT  • (Ability) Use models to identify the causes of congestion • (Ability) Propose traffic management strategies 

The students should have previous knowledge about: • Programming (Python) • Concepts of statistics (distributions, statistical tests, regressions,...) • Networks (basic knowledge of queuing networks and graph theory) • Basics of operational research

Intelligent transport systems and ICT (1 CFU) – Definition of a transport system. Characteristics of transport demand and supply. Impacts of the transport systems (economic, social, environmental). Fundamentals of ITS (standard and architecture) and application to innovation for transport systems, in mobility management, and in the definition of mobility patterns. The relationship between transport systems and ICT. Advanced technologies for public transport management. Future evolution of transport systems. Modelling transport system and traffic flow (2 CFU) – Demand and Supply model. Definition of the study area and zoning. Graph of the transport network model. Trip generation and trip distribution modelling. Modal choice and assignment. Traffic variables to describe congestion and their relations among them. Review of the different families of traffic models (micro, meso, macro, network). Introduction to different car-following models. Description of space and time discretisation. Data collection and analysis (1.5 CFU) - Obtaining mobility data: from surveys to sensors. Open data and real-time data for management and planning of a transport system. Collect data from web systems. Analysis of mobility data: construction of O/D matrixes, definition of traffic zones, analysis of paths. Forecasting future scenarios. Transport management and control (1.5 CFU) - Control techniques. Traffic control for congested networks for single- and multi-region systems. Traffic signal control. Dynamic fixed-time signal plans. Discussion of real-time adaptive strategies. Route and departure time choice. 

The course consists of Lectures (30 hours) and Laboratory sessions (30 hours). The laboratory sessions are focused on the topics of transport modelling and ITS. Students will perform these laboratory activities using their own laptops. Students will work in groups of three, and they will be required to write a report on some of the laboratory assignments.

Copies of the slides used during the lectures, examples of exercises, and manuals for the activities in the laboratory will be made available. All teaching material can be downloaded from the course page in the Teaching Portal. During each lecture, the teachers will also suggest additional reading material (books and research papers) for further deepening the subject.

Slides; Esercitazioni di laboratorio; Esercitazioni di laboratorio risolte; Video lezioni dell’anno corrente; Materiale multimediale ;

Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;

Exam: Compulsory oral exam; Group essay;

... Students will work in groups of three, and they will be required to write a report on some of the laboratory assignments. The group reports must be uploaded to the Teaching Portal. The deadline for the upload is the same as the deadline for registering for the oral exam ("prenotazione esame"). The teachers will evaluate each report, and the grade (maximum 30 cum laude) will be proposed to the group. If the grade is insufficient (less than 18/30), the group must update the report. If sufficient, it can be improved only if all group students agree to do so. The report will be valid for 2 academic years so that students that did not pass the oral exam do not have to prepare another report. Reports will focus on specific topics assigned and discussed during the course. Students will need to prepare a textual document and share the developed code. Specifically, each group will prepare three reports as follows: • Data exploration of sharing systems. Extraction of data from MongoDB, the definition of filtering analytics and characterization of mobility patterns. • Forecasting future scenarios. Extraction of time series from real dataset, fitting and evaluation of regression algorithms for prediction. • Comparison of survey and data-based transport demand. Extraction of origin-destination matrices from data and survey, characterization of obtained demand models and quantitative comparisons of results. Examples of students' reports on similar topics will be made available for students on the Teaching Portal. Each student will have a compulsory oral exam (maximum 30 cum laude) on the topics discussed during the lectures and/or addressed during the exercises and laboratories. The oral examination must be sufficient (at least 18/30). In the oral exam, students will answer questions on the theoretical and practical parts. • For the theoretical part, students will be asked to discuss the methodologies learned during the course and their possible applications, with simple and practical example problems to discuss and solve. • For the practical part, students will discuss laboratories, including the developed code used for preparing the reports, and propose possible improvements and alternatives. The final grade will be given by the weighted average of the oral grade (70%) and the group's report grade (30%). The maximum grade will be 30 cum laude. • Group's report grade (30%, at least 18/30) • Individual oral exam (70%, at least 18/30)

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.