The course, through theoretical lessons and practical works using real data and specific case studies, is focused at giving to the students a scientific approach to transport engineering with a special focus on ITS (Intelligent Transport Systems), helping them to acquire an integrated vision among transport and ICT. After a first brief introduction on the concept of transport systems and transport planning, the monitoring methods of transport systems as well the data collection and the techniques to describe the system demand-supply both through mathematical models and demand-supply interaction models will be presented. The main part of the course is focussed on the issues related to the implementation of sustainable transport systems and to the transport demand management, considering the interaction between transport and ICT as well the evaluation of the effects of ITS and new technologies on travel behaviour. The course gives an overview of ITS, technological innovations and their applications to different transport modes. Then, the traveller information systems are presented with a main focus on the data needed to design them and a special attention to big and open data. Case studies and examples of applications will support the full understanding of the field. Students will be required to put into practice the lessons in exercises using ICT platforms for managing transport systems. Part of the course is devoted to the development of a case study where students are required to complete some comprehensive cases study related to the world of transport.

The knowledge acquired all along the course is both methodological and applied. The methodological knowledge is based on theories and methods allowing the development of the ability to design "smart" transport systems through the ICT use and to critically analyse the existing transport systems to make them progressing towards a greater sustainability, also thanks to the support of the new technologies. More precisely, the student will be able to explain the main components and technologies of ITS and to relate them with the key concepts and components of the ITS systems architecture and the basic design of traveller information systems, for all transport modes. Furthermore, the study of the data collection methods and the use of big data allows to analyse the mobility under a new perspective, evaluating when and how such data can be used for forecasting and which is their reliability in regards to the traditional methods. The applied knowledge is acquired through the experimental work carried out during the course that provides the analysis of real cases to which apply the learned theories. For example, the students learn how to calculate the transport costs in different scenarios, allowing the development of the ability to forecast the users’ modal choice and to design information systems facilitating the trips using different transport modes (intermodality). The theoretical knowledge acquired during the course allows to develop: a) the ability to consider mobility within a complex framework in which ICT interacts with transport systems; b) the ability to carry out applications allowing to decision makers a better management of mobility and to users a better planning of their trips, making them more sustainable; c) the ability to analyse mobility data (also big data), through advanced statistical techniques and data mining, and to use them for giving specific and tailored information to users; d) the ability to evaluate the effects of ICT on travel behaviour and to find out the limits of their development; e) the ability to understand the implementation of ITS applications to public transport, to fleets management, to traveller information systems and to the transport demand management.

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 cases study, the student must have knowledge concerning networks and the normal programming language, particularly of the Python language.

INTRODUCTION TO TRANSPORT SYSTEM (4,5 hours) • Definition of a transport system: territorial system of the activities (attraction) and of residences (generation); sub-systems 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 system (economical, social, enviromental) NEW TECHNOLOGIES FOR INTELLIGENT TRANSPORT SYSTEM (ITS) (13,5 hours) • Main regulations of ITS and future evolution of transport systems. The relationship between transport systems and ICT, with emphasis on ITS (Intelligent Transport Systems) (1,5 hours) • Fundamentals of ITS (standard and architecture) and application to innovation for transport systems, in mobility management, and in the definition of mobility patterns (1,5 hours) • Technologies for public transport management (6 - 9 hours) 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. • ICT solutions for transport system management (smart mobility and smart cities, involving end-users into the decision process via apps, impact of ICT on end-user habits) (1,5 hours) MODELS FOR TRANSPORT SYSTEMS (6 hours) • Supply model (1,5 hours) definition and zoning of the Plan and the Study area; zone sizing; graph of the transport network model (centroids; nodes of the network, links of the network, connecting links, flows, costs, paths). • Demand models (4,5 hours) General structure of the demand models. Multiple stage models: generation models, distribution models, modal choice models, route choice models / assignment. Behavioural models of discrete choice and random utility (logit and probit). OPEN DATA AND REAL TIME DATA FOR MANAGEMENT AND PLANNING OF A TRANSPORT SYSTEM (3 hours) • Sources of open data and real time data (1,5 hours) • Decision Support System and Decision Support Tool, definition of Key Performance Indicators (KPIs) and visualization of KPI in dashboards (1,5 hours) DATA COLLECTION AND DATA ANALYSIS THROUGH THE NEW TECHNOLOGIES (13 hours) • Use of smartphone apps as a collaborative tool for collecting mobility data (1 hour) • Analysis of mobility data collected through Google: construction of O/D matrixes, definition of traffic zones, analysis of paths, matching the paths with the used transport mode (4,5 hours) • Individuation of the transport mode from the GPS data (4,5 hours) • Analysis of textual data appearing in social networks: how to extract information from text (3 hours) CASE STUDY – CAR SHARING (20 hours) Students will develop a case study considering car sharing system, and in particular, they will learn and put in practice how to develop an application to • Collect data from web systems • Store information in NO SQL database – The usage of MongoDB • Analysis of collected data.

Teaching sees frontal lessons alternating with practical exercises through the use of software and computers. Some topics will be addressed by industry experts who will directly present some topics. The final project will be done using the students' PCs that will be called to implement simple programs to collect, store, and analyze 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 nature of the course and the available references do not allow to have only one textbook and the attendance to the course is fundamental for an effective learning process. During the course (at each time) proper textbooks, in English and Italian, will be suggested to complete the training process. As an example, some topics are contained in the following textbooks: - ICT for transport: opportunities and threats. Thomopoulos N., Givoni M., Rietveld P. (Eds.). NECTAR series on Transportation and Communications Networks Research, Cheltenham: Edward Elgar. 2015. ISBN 978 1 78347 128 7. - Modelling Transport, 4th Edition. Juan de Dios Ortúzar, Luis G. Willumsen. Wiley 2011. ISBN: 978-0-470-76039-0 - Urban Transportation Planning, MEYER M., MILLER E.J. McGraw-Hill 2001 Concerning specific topics, ad hoc material (articles, reports, etc.) will be uploaded on the Politecnico web site (teaching portal).

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

Exam: Compulsory oral exam; Group essay;

... Students will have to prepare a report on the Laboratory Part (Group Report) whose content will be indicated during the lectures by the teacher. The group report has to be uploaded on the didattica portal in pdf format. The deadline for the upload is the same as the deadline for registering to the exam (prenotazione esame). It is possible to upload multiple version 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 report 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 exercises. The latter are based on the use of data and / or maps provided by the teacher and will be evaluated by assigning a vote (maximum 30 and praise). 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 lab report 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 (65%) and the group's report grade (35%). The maximum vote will be 30 cum laude. No mid term homeworks are required

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.

Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;

Students will have to prepare two reports on the Laboratory Part (Group Report) whose content will be detailed during the lectures by the teacher (data analysis, and prediction model). The group report has to be uploaded on the didattica portal in pdf format. The deadline for the upload is the same as the deadline for registering to the exam (prenotazione esame). Each report will be corrected and the grade (maximum 30 and praise) will be proposed to the group. Once corrected, the report 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 have an oral exam on the topics discussed during the course for in-depth discussion of topics discussed in lessons and / or addressed during the exercises. The latter are based on the use of data and / or maps provided by the teacher and will be evaluated by assigning a vote (maximum 30 and praise). 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 lab report 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 (65%) and the group's report grade (35%). The maximum vote will be 30 cum laude.

Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;

Students will have to prepare two reports on the Laboratory Part (Group Report) whose content will be detailed during the lectures by the teacher (data analysis, and prediction model). The group report has to be uploaded on the didattica portal in pdf format. The deadline for the upload is the same as the deadline for registering to the exam (prenotazione esame). Each report will be corrected and the grade (maximum 30 and praise) will be proposed to the group. Once corrected, the report 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 have an oral exam on the topics discussed during the course for in-depth discussion of topics discussed in lessons and / or addressed during the exercises. The latter are based on the use of data and / or maps provided by the teacher and will be evaluated by assigning a vote (maximum 30 and praise). 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 lab report 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 (65%) and the group's report grade (35%). The maximum vote will be 30 cum laude.