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Transport systems and data analytics/Transport planning
02VKVMX, 02VKVBH
A.A. 2023/24
Transport systems and data analytics/Transport planning (Transport Planning)
The main objective of the course is to teach and to train students to deal with transport planning activities through a scientific approach. The main steps of the transport modeling and planning process are presented at lesson and contextually developed by students through workshops on a real case study. Special attention is paid to those aspects that are critical for the quality of a transport plan, from data collection issues to the appropriate selection of a model for the problem instance under consideration. The last part of the course enlarges the perspective to show to students the interactions between transport systems and economics, land use and environmental issues, and how these interactions impact evaluation activities of a transport scenario.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.
Transport systems and data analytics/Transport planning (Transport Planning)
The main objective of the course is to teach and to train students to deal with transport planning activities through a scientific approach. The main steps of the transport modeling and planning process are presented at lesson and contextually developed by students through workshops on a real case study. Special attention is paid to those aspects that are critical for the quality of a transport plan, from data collection issues to the appropriate selection of a model for the problem instance under consideration. The last part of the course enlarges the perspective to show to students the interactions between transport systems and economics, land use and environmental issues, and how these interactions impact evaluation activities of a transport scenario.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.
Transport systems and data analytics/Transport planning (Transport Planning)
Ability to analyze mobility data through basic statistical techniques. Ability to understand the ambits of use and limitations of the most common transport planning models and methods to forecast travel demand and its interaction with the supply. Ability to carry out the most common quantitative analyses that are normally needed to set up a rationale transport planning process. Ability to consider and to quantitatively evaluate some key aspects of the mobility phenomenon within a complex framework in which economic, territorial and environmental issues interfere with transport systems.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.
Transport systems and data analytics/Transport planning (Transport Planning)
Ability to analyze mobility data through basic statistical techniques. Ability to understand the ambits of use and limitations of the most common transport planning models and methods to forecast travel demand and its interaction with the supply. Ability to carry out the most common quantitative analyses that are normally needed to set up a rationale transport planning process. Ability to consider and to quantitatively evaluate some key aspects of the mobility phenomenon within a complex framework in which economic, territorial and environmental issues interfere with transport systems.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.
Transport systems and data analytics/Transport planning (Transport Planning)
Basic algebra and analysis (matrices, derivatives, integrals). Fundamentals of statistics (probability distributions, cumulative distribution functions, mean, variance, standard error, coefficient of variation, confidence intervals, regression and least squares, the central limit theorem). Fundamentals of economics (demand and offer curves, project financial evaluation methods, cost-benefit analysis). Concerning informatics, advanced use of a spreadsheet, fundamentals of computer programming and eventually basic notions concerning GIS (geographic information systems).
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Basic notions on transport systems provided in the Transport Economics and Technique subject, besides consolidated bases of engineering (Maths, Physics and Chemistry).
Transport systems and data analytics/Transport planning (Transport Planning)
Basic algebra and analysis (matrices, derivatives, integrals). Fundamentals of statistics (probability distributions, cumulative distribution functions, mean, variance, standard error, coefficient of variation, confidence intervals, regression and least squares, the central limit theorem). Fundamentals of economics (demand and offer curves, project financial evaluation methods, cost-benefit analysis). Concerning informatics, advanced use of a spreadsheet, fundamentals of computer programming and eventually basic notions concerning GIS (geographic information systems).
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Basic notions on transport systems provided in the Transport Economics and Technique subject, besides consolidated bases of engineering (Maths, Physics and Chemistry).
Transport systems and data analytics/Transport planning (Transport Planning)
1. Introduction and surveying phase (9 hours of lessons and 15 hours of workshops) • Origins of the discipline. Some characteristics of travel demand: derived goods, relationship with the economic development, travel time budgets. The main stages in the transport planning process: surveying, simulation and modelling, evaluation and forecast. • Definition of study area and enlarged study area. The notions of trip, tour, stage. Trip representation: zoning and zoning criteria. Definitions of trip origin and destination, O/D matrix, trip productor and attractor. Representation of transport networks: concept of graph, centroid and its localisation, centroid connector, arc attributes. • Passenger travel demand surveys: kinds of surveys and questionnaire design. Simple, stratified and choice-based sampling. 2. Simulation phase: transport models (16 hours of lessons and 13 hours of workshops) • Simulation phase: transport model definition. Concepts of specification, estimation, calibration and validation of passenger travel models. Models classifications. Kinds of errors in models. Introduction to the four-step model. • Trip generation. Classification of trips. Aggregate trip regression models for attractions (zoning level) and disaggregate trip regression models for productions (household or individual level). Non linearity and aggregation of the results. Category analysis models. Balancing trip production and attraction models. • Trip distribution. Recall on generalised trip costs and value of travel time. Singly and doubly constrained models. Growth factor methods. The gravity model. • Modal split: aggregate models. Factors that influence the choice of the transport mode. Notes on aggregate modal split models and their positioning within the four-step transport planning process. Notes on synthetic trip distribution and modal trip models and on direct demand models. • Modal split: disaggregate discrete choice models. Logistic regression and linear probability model, logit and probit model formulation. Consumer theory: concept of random utility, related assumptions and subsequent characteristics of random utility models. The multinomial logit model: statistical assumptions on the distribution of residuals and subsequent limitations of the model, independence from irrelevant alternatives. Heteroskedastic and correlated error structures: the hierarchical or nested logit model, the cross-nested logit model. The multinomial probit model. The mixed logit model and choice models with latent variables: key concepts. • Using discrete choice models: model specification, choice set definition, model estimation, aggregation problems. Non-compensatory choice protocols: dominance, satisfaction and lexicographic rules; role of habit. • Trip assignment. Relationship between costs and flows, path choice mechanism and minimum spanning tree, demand-supply equilibrium. Deterministic assignment methods without capacity constraints: all-or-nothing. Notes on the stochastic methods of multipath assignment. Capacity constrained assignment, Wardrop principles and Braess’s paradox. Limitations of the classical trip assignment methods. • The four-steps model critique and activity-based approaches. Model implementations: notes on the transport planning software packages that are based on trips and zoning and on those based on agents and microsimulation. 3. Building transport planning scenarios: evaluation and forecast (7 hours of lessons) • Evaluation phase of transport projects, scenarios and plans. The dimensions of an evaluation process, cost benefits versus multicriteria analysis, impact indicators. An example of impacts quantification: emissions and dispersion models of air pollutants. The monetization of the impacts: external and social costs, travel time value and willingness to pay. • Forecast phase. Spatial and temporal transferability of demand and supply model parameters. Updating the model exogenous variables. Transport and land use, accessibility. Induced traffic. Travel demand and mobility management tools: regulations, pricing, offer of new services. • Transport planning process and documents in Italy. Planning at the national level: national transport plan, SIMPT, SNIT. Planning at the regional and local level: regional transport plan, sustainable urban mobility plans, mobility management.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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)
Transport systems and data analytics/Transport planning (Transport Planning)
1. Introduction and surveying phase (9 hours of lessons and 15 hours of workshops) • Origins of the discipline. Some characteristics of travel demand: derived goods, relationship with the economic development, travel time budgets. The main stages in the transport planning process: surveying, simulation and modelling, evaluation and forecast. • Definition of study area and enlarged study area. The notions of trip, tour, stage. Trip representation: zoning and zoning criteria. Definitions of trip origin and destination, O/D matrix, trip productor and attractor. Representation of transport networks: concept of graph, centroid and its localisation, centroid connector, arc attributes. • Passenger travel demand surveys: kinds of surveys and questionnaire design. Simple, stratified and choice-based sampling. 2. Simulation phase: transport models (16 hours of lessons and 13 hours of workshops) • Simulation phase: transport model definition. Concepts of specification, estimation, calibration and validation of passenger travel models. Models classifications. Kinds of errors in models. Introduction to the four-step model. • Trip generation. Classification of trips. Aggregate trip regression models for attractions (zoning level) and disaggregate trip regression models for productions (household or individual level). Non linearity and aggregation of the results. Category analysis models. Balancing trip production and attraction models. • Trip distribution. Recall on generalised trip costs and value of travel time. Singly and doubly constrained models. Growth factor methods. The gravity model. • Modal split: aggregate models. Factors that influence the choice of the transport mode. Notes on aggregate modal split models and their positioning within the four-step transport planning process. Notes on synthetic trip distribution and modal trip models and on direct demand models. • Modal split: disaggregate discrete choice models. Logistic regression and linear probability model, logit and probit model formulation. Consumer theory: concept of random utility, related assumptions and subsequent characteristics of random utility models. The multinomial logit model: statistical assumptions on the distribution of residuals and subsequent limitations of the model, independence from irrelevant alternatives. Heteroskedastic and correlated error structures: the hierarchical or nested logit model, the cross-nested logit model. The multinomial probit model. The mixed logit model and choice models with latent variables: key concepts. • Using discrete choice models: model specification, choice set definition, model estimation, aggregation problems. Non-compensatory choice protocols: dominance, satisfaction and lexicographic rules; role of habit. • Trip assignment. Relationship between costs and flows, path choice mechanism and minimum spanning tree, demand-supply equilibrium. Deterministic assignment methods without capacity constraints: all-or-nothing. Notes on the stochastic methods of multipath assignment. Capacity constrained assignment, Wardrop principles and Braess’s paradox. Limitations of the classical trip assignment methods. • The four-steps model critique and activity-based approaches. Model implementations: notes on the transport planning software packages that are based on trips and zoning and on those based on agents and microsimulation. 3. Building transport planning scenarios: evaluation and forecast (7 hours of lessons) • Evaluation phase of transport projects, scenarios and plans. The dimensions of an evaluation process, cost benefits versus multicriteria analysis, impact indicators. An example of impacts quantification: emissions and dispersion models of air pollutants. The monetization of the impacts: external and social costs, travel time value and willingness to pay. • Forecast phase. Spatial and temporal transferability of demand and supply model parameters. Updating the model exogenous variables. Transport and land use, accessibility. Induced traffic. Travel demand and mobility management tools: regulations, pricing, offer of new services. • Transport planning process and documents in Italy. Planning at the national level: national transport plan, SIMPT, SNIT. Planning at the regional and local level: regional transport plan, sustainable urban mobility plans, mobility management.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
1. Data collection in the transport sector and related reliability, bases (9 hours) [B. dalla Chiara] - Technologies, methods and ITS (Intelligent Transport Systems): TLC for transport systems; automatic vehicle location systems (AVLS), automatic identification systems (AEI/AVI), traffic data collection and automatic passenger courting (APC) to automatically and passively collect data on mobility and logistics, from conventional transport services 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 - 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 - 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 - Trends, product categories, type of ships, stakeholders involved - Naval gigantism: reasons, effects on maritime and inland infrastructures, limits. 3. Data-driven methods for subsequent engineering and decision making in the transport sector (9 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 [S. Gurri]: - On board an automobile; OBD II scanners on a CAN bus (3 h) - Matlab and Simulink; traceability of data and requirements through the engineering and design of a transport system or a complex vehicle (3 h) 4. Data analytics (19.5 hours) a. Clustering techniques and tools b. Knowledge discovery in databases c. Data mining and data fusion d. Machine learning e. Practical applications: explanation, work groups, de-briefing and explanation of results 5. 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).
Transport systems and data analytics/Transport planning (Transport Planning)
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Transport systems and data analytics/Transport planning (Transport Planning)
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Transport systems and data analytics/Transport planning (Transport Planning)
Theoretical lessons will last about 30 hours, according to the above mentioned breakdown. Each lesson covering one of the above mentioned topics in sections 1 and 2 (surveying and simulation phases) will be immediately followed by a workshop to apply the acquired knowledge by developing a case study with real data. Eight workshops will therefore be held during the course. Each workshop will last 3-4 hours and it will deal with a particular task in the transport planning process, but all workshops will be based on the same case study and data and therefore follow a sequential structure. During workshops, students will have to replicate on their own laptop what is done by the instructor, irrespective of the teaching means (onsite, online or blended). Then, they will have to work in groups to fully develop the proposed activity. Workshop homework will be assigned and must be turned in shortly after for correction. Homework that is not sufficiently developed or contains errors will be rejected and the group will have to resubmit it until it is approved. Each workshop builds on the results of the previous ones, so it is essential to stick to the deadlines.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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).
Transport systems and data analytics/Transport planning (Transport Planning)
Theoretical lessons will last about 30 hours, according to the above mentioned breakdown. Each lesson covering one of the above mentioned topics in sections 1 and 2 (surveying and simulation phases) will be immediately followed by a workshop to apply the acquired knowledge by developing a case study with real data. Eight workshops will therefore be held during the course. Each workshop will last 3-4 hours and it will deal with a particular task in the transport planning process, but all workshops will be based on the same case study and data and therefore follow a sequential structure. During workshops, students will have to replicate on their own laptop what is done by the instructor, irrespective of the teaching means (onsite, online or blended). Then, they will have to work in groups to fully develop the proposed activity. Workshop homework will be assigned and must be turned in shortly after for correction. Homework that is not sufficiently developed or contains errors will be rejected and the group will have to resubmit it until it is approved. Each workshop builds on the results of the previous ones, so it is essential to stick to the deadlines.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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).
Transport systems and data analytics/Transport planning (Transport Planning)
The reference book is Ortuzar, J.d.D. and Willumsen, L.G. (2011) Modelling Transport – 4th edition, Wiley, ISBN 978-0-470-76039-0, although the course covers less than half of its contents. A lot of material from other sources is needed and it will be distributed by the instructor through the course webpage.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.
Transport systems and data analytics/Transport planning (Transport Planning)
The reference book is Ortuzar, J.d.D. and Willumsen, L.G. (2011) Modelling Transport – 4th edition, Wiley, ISBN 978-0-470-76039-0, although the course covers less than half of its contents. A lot of material from other sources is needed and it will be distributed by the instructor through the course webpage.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Lecturer's handouts on the topics covered, distributed during the course of the lectures References: [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.
Transport systems and data analytics/Transport planning (Transport Planning)
Dispense; Libro di testo; Esercizi risolti; Esercitazioni di laboratorio; Strumenti di simulazione;
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Slides; Dispense; Libro di testo; Esercitazioni di laboratorio; Esercitazioni di laboratorio risolte;
Transport systems and data analytics/Transport planning (Transport Planning)
Lecture notes; Text book; Exercise with solutions ; Lab exercises; Simulation tools;
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Lecture slides; Lecture notes; Text book; Lab exercises; Lab exercises with solutions;
Transport systems and data analytics/Transport planning (Transport Planning)
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Transport systems and data analytics/Transport planning (Transport Planning)
Exam: Compulsory oral exam; Group project;
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Exam: Compulsory oral exam; Group essay;
Transport systems and data analytics/Transport planning (Transport Planning)
As a precondition to take the exam, homework from all eight workshops must be previously approved by the instructor. The exam is an individual oral colloquium of about 45 minutes, during which homework outcomes are discussed (15 minutes) and some questions on the theoretical part of the course are asked (30 minutes). Questions are aimed at assessing both the knowledge reached by students on the above listed topics and the abilities related to the above listed learning outcomes. The final evaluation keeps into account both the quality of the work done during the course and the performance during the final oral exam.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.
Transport systems and data analytics/Transport planning (Transport Planning)
Exam: Compulsory oral exam; Group project;
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Exam: Compulsory oral exam; Group essay;
Transport systems and data analytics/Transport planning (Transport Planning)
As a precondition to take the exam, homework from all eight workshops must be previously approved by the instructor. The exam is an individual oral colloquium of about 45 minutes, during which homework outcomes are discussed (15 minutes) and some questions on the theoretical part of the course are asked (30 minutes). Questions are aimed at assessing both the knowledge reached by students on the above listed topics and the abilities related to the above listed learning outcomes. The final evaluation keeps into account both the quality of the work done during the course and the performance during the final oral exam.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
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.