The knowledge acquired all along the course refers to innovation in transport systems and methods for data analytics. The collection and analysis of data (and big data) will allow obtaining useful information to provide original insights into, for example, the state of the transport market (transport demand), the travel behaviour, how to refine traveller experience strategies and so on. To this end, the students will be able to: - use tools to acquire data thanks to several channels: apps, sensors, etc. (IoT); - store data through the construction of databases; - process information thanks to advance statistical methods (e.g. multivariate statistical analysis) and data mining techniques, identifying correlations, patterns and trends in big volumes of data through neural networks, decision tree and analysis of associations; focusing on dedicated applications for transport sector, services (e.g. to customise technologies and successfully integrate them into existing systems) and infrastructural resources (computing capacity, storage, etc.). The capacity of data collection and analysis will be acquired together with the ability of ensuring data privacy and security to critically analyse the existing transport systems to make them progress towards a greater sustainability, also thanks to digital transition. The competence acquired will allow students to; • conceive an intelligent transport system; • design and develop mobility services and solutions; • lead a "Digital Transition" project in the field of transport; or • conduct an "intelligent transport system" project in a company or a local authority; and to work in professional contexts as:  communities and transport authorities;  mobility service providers;  operators transporting people or goods;  infrastructure managers;  industrial manufacturers or automotive equipment manufacturers;  digital industry: IT services companies, software publishers, computer manufacturers, telecom operators and equipment manufacturers.

The student must possess a good computer knowledge and the foundations of mathematics. It is also imperative that the student master the basic concepts of statistics. Regarding knowledge in transport discipline, it would be preferable for the student to have already acquired the basic of the discipline. For the development of the case studies, the student must have knowledge concerning the normal programming language, particularly of the Python language, and preferably data mining techniques as well.

The course will be based on concept of learning by doing. Theoretical lectures (42h) will be followed by practical application with work on data (18h). INTRODUCTION TO TRANSPORT SYSTEMS (3 hours) Definitions and fundamentals of the discipline of transport. What are transport and mobility. Concepts of transport demand and transport supply. Characteristics of the transport demand (derived, not-derived, induced, latent) and of the transport supply (infrastructures and services). Impacts of the transport systems (economic, social, environmental). Analysis of the main trends in the mobility of people and goods and their main determinants. INTELLIGENT TRANSPORT SYSTEMS (ITS) (9 hours) Definition, technical and legal evolution; regulation. Fundamentals of ITS (standard and architecture) and application to innovation for transport systems, in mobility management, and in the definition of mobility patterns. Key underlying technologies of ITS for public transport management: a) Advanced Communications Systems (ACS); b) Automatic Vehicle Location (AVL) Systems; c) In-Vehicle Diagnostic Systems; d) Automatic Passenger Counter Systems; e) Traffic Counter Systems f) Electronic Payment Systems; g) Real time fleet management systems; h) Connected vehicles (V2V, V2I) and Autonomous vehicles. Communication and protocols. DATA COLLECTION AND INFORMATION PROCESSING (15 hours) Methods to identify and collect data from different sources will be covered. Methods ranging from traditional surveys to digital methods including: • use of smartphone apps as a collaborative tool for collecting mobility data; • data from smart cards; • sensors & IoT; • open data and real time data of transport systems and services. Standardisation of the collected data format where possible and use of data fusion techniques will also be covered. Other related topics that will be touched upon include data storage options, data privacy and security. Introduction to big data and big data analytics for transport sector. From data-driven models to decision intelligence and cognitive era. Use of big data analytics techniques to provide concrete applications for transport operators, to understand the state of the market, the transport demand, the travel behaviour and to better tailor the mobility services to improve user experience. Data analysis approach that is descriptive, predictive, prescriptive, i.e. exploiting big data analytics applications through which to generate 'insights', knowledge useful for decision-making processes (e.g. anticipating customers' needs by knowing their preferences and habits in real time). NEW MOBILITY SERVICES AND THE DIGITAL TRANSITION (6 hours) The new forms of mobility: shared, electric, automated. Technical and economical characteristics and general view about the transformations induced by the new mobility services carried by digital platforms. Actors, policies, legal aspects, cybersecurity. DIGITAL PLATFORMS AND MaaS (MOBILITY as a SERVICE) (9 hours) Digital platforms in the transport sector. Mobility as a Service: definition, ecosystem and typology. Benchmark of different MaaS across the world: limit and challenges. MaaS Stakeholders: roles and expectation. Business models of MaaS. CASE STUDIES (practical part) (18 hours) SHARED MOBILITY (6 hours) Collection of data from car sharing, bike-sharing, scooter sharing. Construction of data bases for analysing data to represent the transport demand, the travel behaviour and the effect on the territory in terms of land use, costs and impacts. CALCULATION OF TRAVEL COST IN DIFFERENT SCENARIOS (6 hours) Calculation of generalised cost using different transport modes in differemt scenarios. DIGITAL PLATFORMS AND MaaS (6 hours) Design of a MaaS and definition of the MaaS architecture using design techniques as service blueprinting. Definition of business model. The practical part will be held in the laboratories (LAIB) or, alternatively, the needed software will be usable also at home, being licensed to Politecnico and, in any case, mainly open source software and tools will be used.

Teaching sees frontal lessons alternating with practical application and exercises through the use of software and computers. Some topics will be also addressed by industry experts who will bring their expertise on some topics. The practical work will be done using the students' PCs. Students will be called to implement simple programs to collect, store, and analyse the data. Students will work in groups of three, and they will be required to write a report on some of the laboratory experiences. The contents of these will be indicated during lectures by the teacher. The above structure will be maintained both in case of physical and online lectures. Indeed, the software will be usable also at home, being licensed to Politecnico and, in any case, mainly open source software and tools will be used.

The nature of the course and the available references do not allow to have a textbook and the attendance to the course is fundamental for an effective learning process. During the course (at each time) the teacher will upload in the teaching portal a rich range of material, formed by reports, scientific articles, slides as well as open access software and manuals. There will be a section with material for the course and a section with additional material for whom interested to deepen the topics of the course for pure sake of additional knowledge.

Exam: Compulsory oral exam; Group essay; Group project;

Exam: compulsory oral exam; group report for each case study. Students will have to prepare a report for each Case study (group report) whose content is indicated in the syllabus (practical part). The group reports have to be uploaded on the teaching portal in pdf format. The deadline for the upload of each report is fixed by the teacher after each Case study. It will not possible to register to the exam of not all the group reports have been uploaded at the moment of the registering for the exam. It is possible to upload multiple versions of the report, however only the last one will be considered. Each report will be corrected and the grade (maximum 30 and praise) will be proposed to the group. Once corrected, the reports can be changed/updated only if all students of the group agree to do so. The report will be valid for 2 years so that students that did not pass the oral exam do not have to prepare another report. Each student will then have an oral exam on the topics faced during the course for in-depth discussion of topics discussed in lessons and/or addressed during the case studies. During the individual oral exam, the student will have to answer up to four questions on the topics presented during the classes. During the oral exam, the student will also discuss the group reports by presenting some of the results he/she obtained and described in the report. The oral examination must be sufficient and over 18/30. The final vote will be given by the weighted average of the oral vote (80%) and the average of group's reports grade (20%). The maximum vote will be 30 cum laude.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.