The subject is intended to provide students with skills related to emerging issues dealing with the planning, operation and management of innovative transport systems, where data have a relevant role. The subject will analyse the opportunities related to the increasing availability of data on both logistics, for freight, mobility of passengers through the diffusion of both on-board and nomadic ICT devices, and how this is likely to impact the professional practice of transport engineers. Staring from reliability of data, the subject is rich of numerical and practical applications, mainly related to optimisation for freight transport and logistics, systems engineering in transport systems, clustering techniques, data mining and fusion and machine learning, in general, as a basis of artificial intelligence. Emphasis will be given on: - the role of freight transport and logistics in shaping a sustainable transport system at all scales, from continental to urban areas; - new mobility services for passengers going beyond the conventional distinction between public and private transport modes.

The student who successfully follows the teaching acquires: - Knowledge of the main characteristics of freight and intermodal transport systems; - Ability to analyse mobility systems through data-driven and optimisation techniques for their design and operation; - Ability to understand how new mobility services can contribute in shaping modern transport systems. - Awareness on the main challenges that contemporary transport systems must face and on their likely evolution in the near future, specifically concerning urban areas. - Practical use of instruments for data collection, analysis and transport engineering, from on-board devices to systems engineering tools, passing through clustering techniques, data mining and machine learning. At the end of the course, students will be able to: - know how to optimise the movement of goods - or even people - on the basis of real transport needs; - optimise a flow of goods between various origins and destinations on the basis of availability, uses, costs and boundary constraints; - apply machine learning and clustering methods; - use system engineering tools to track requirements and data along a process of design and functional testing of a transport system (Mathlab); - use devices to collect data from on-board a vehicle to be able to analyse the relevant variables of motion, energy consumption, emissions, etc.

Basic notions on transport systems provided in the Transport Economics and Technique subject, besides consolidated bases of engineering (Maths, Physics and Chemistry).

1. Data collection in the transport sector and related reliability, bases (3 hours) [B. dalla Chiara] - Technologies and methods to automatically and passively collect mobility data, from conventional to most advanced ones. - Mobility data sources and big data. - Systems engineering approach: principles, quality and traceability of data - Planning and applications of new mobility services and freight transport. - Data formats and standardisation issues, open data, repositories. 2. Transport systems for freight, related data and optimisation techniques (21 h) [C. Caballini] A. Transport modes for freight (6 hours) - Hub and spoke networks with conventional and intermodal transport - Freight intermodal transport - Convenience between single-mode and intermodal cycle - Case study: “Logistics and freight transport: time, cost and environmental impact”, to be solved with excel and an on-line tool B. Data and optimisation techniques in logistics and transport (10.5 hours) - Introduction to optimisation techniques - Some logistic problems and related analytical formulation (including hints to urban distribution of goods) - Resolution with a solver and with software packages (adoption of Lingo package), with applications. - Explanation of case studies, work groups, de-briefing and explanation of results C. Port-maritime logistics (4.5 hours) - Trends, product categories, type of ships, stakeholders involved - Naval gigantism: reasons, effects on maritime and inland infrastructures, limits. 3. System Engineering and the MBSE (model based systems engineering) in the transport domain (3 hours) [S. Gurrì] - Tools of the MBSE. - Introduction to System Composer. - Functional, logical and physical architecture of a system. - Allocation between models for traceability; Stereotypes to enable a Domain Specific language. - Requirements: what they are, requirement types, verification and validity of requirements. - Traceability of requirements along the product life cycle. Complex systems and asynchronicity of their management. Bottom-up and top-down design approaches. - Matlab and Simulink; traceability of data and requirements through the engineering and design of a transport system or a complex vehicle 4. Data-driven methods for subsequent engineering and decision making in the transport sector (6 h) A. Reliability of data, their measurement, frequency and sampling mode of a physical phenomenon within the transport domain (3 h) [A. Vallan] B. Data from on board units and traceability through the Systems engineering approach; practical applications: explanation, work groups, de-briefing and explanation of results: on board an automobile; OBD II scanners on a CAN bus (3 h) [S. Gurrì] 5. Data analytics (19.5 hours) [Flavio Giobergia, ING-INF/05] a. Data analytics b. Data quality: noise, outliers, missing values c. Knowledge discovery in databases d. Data Science Pipeline, in general and for clustering e. Clustering techniques and tools f. Data mining and data fusion g. Machine learning h. Practical applications in the transport domain: : explanation, work groups, de-briefing and explanation of results. 6. Present services and perspectives in the transport domain (3 hours) [B. dalla Chiara] A. Integrated services and data for co-modality and synchro-mobility, Mobility as a Service (MaaS): concept, case studies - Co-modality and multimodality, seamless travel and interoperability. - Technological prerequisites, role of information and technology, technological platforms. - Business models for MaaS and the role of transport decision makers. Legal and policy issues. B. Shared mobility and technological evolution - Main concept. Sharing economy and mobility: facts and figures - Car sharing operational variants and business models. - Sharing economy/mobility threats. - The evolution of sharing mobility towards shared autonomous vehicles (SAV). 7. Technical visit and/or seminar concerning the subjects of the course (3 hours)

The course includes theory, practical exercises and applications concerning freight transport and new mobility services for passengers: explanation, group work, de-briefing and explanation of results. During the semester, exercises, numerical applications relating to topics covered during the lectures and relevant to the course topics are carried out. Students, in small groups, are required to write a report on one of the main subjects faced during the semester (optimisation concerning freight transport; OBD-II on automobiles; systems engineering; clustering/data mining and fusion/machine learning).

Lecturer's handouts on the topics covered, distributed during the course of the lectures [1]. Dalla Chiara B. (2021) ITS for Transport Planning and Policy. In: Vickerman, Roger (eds.) International Encyclopedia of Transportation vol 6. pp. 298-308. United Kingdom: Elsevier Ltd; [2]. Ghiani G., Laporte G., Musmanno, R. (2013). “Introduction to logistics systems management”. John Wiley & Sons. [3]. Lindo systems Inc., LINDO (2020), The modeling language and optimizer, Handbook for the use of the software Lingo, https://www.lindo.com/downloads/PDF/LINGO.pdf, 2020 [4]. Mathworks (2022), Get Started with System Composer - Design and analyze system and software architectures, https://it.mathworks.com/help/systemcomposer/getting-started-with-system-composer.html [5]. Vickerman, R. (eds.) (2021) International Encyclopedia of Transportation, United Kingdom, Elsevier Ltd, 2021 (Data analytics) [6]. Williams H. Paul (2013): “Model building in mathematical programming”, John Wiley & Sons.

Slides; Dispense; Libro di testo; Esercitazioni di laboratorio; Esercitazioni di laboratorio risolte;

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

Exam: Compulsory oral exam; Group essay;

... Compulsory oral exam; group essay. Learning is reflected both in the writing of the work-group activity and in the performance of short classroom exercises aimed at solving recurring problems in the analysis of transport data. The examination consists of the writing and delivery of the aforementioned short documents (one for each group), carried out in small groups and agreed at the beginning of the teaching period, with a subsequent oral test on the programme. There is no written test. Booking on the teaching portal is compulsory; if a student is unable to attend the oral examination, booking must be cancelled. The oral examination can only be taken in the event of a mark of at least 6/10 on group-document. The knowledge assessment is carried out with at least two questions on the developed part of the syllabus and is supplemented by the evaluation of the previously handed in application papers. The maximum mark is 30/30.

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.