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Transport systems and data analytics/Transport planning
01VKVVA, 01VKVMX, 01VKVWO, 01VKVXG
A.A. 2025/26
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 criticai far 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 course is designed to provide students with a solid and comprehensive understanding of how dota con be effectivety leveraged to plan, operate, and manage innovative transport systems. In an era characterized by the exponential growth of data availablity-enabled by on-boord sensors, mobile technologies ond digital platforms-students will gain the analytical skills and technical tools needed to analyze and interpret complex transport and mobility systems and, thus, make informed, data-driven decisions. The course focuses on integrating data analytics into transport engineering practices, covering both freight and passenger transport systems. Beginning with an overview of transport system typologies ond the fundamentals of data reliability and quality, the course introduces students te a braod range of quantitative methods ond practical tools, including: • mathematical optimization techniques for freight transport and logistics; • systems engineering approach for the design and management of transport networks; • machine learning and data mining methods, with practical opplications such as linear regression, clustering ond classification. To reinforce the learning process, the course includes group work and interactive hands-on classroom activities, aimed at developing analytical thinking, teamwork and problem-solving skills. By the end of the course, students will have acquired a data-oriented mindset, along with the ability to model, analyze, and support strategic and operational decisions in complex tronsport scenarios.
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 criticai far 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 course is designed to provide students with a solid and comprehensive understanding of how dota con be effectivety leveraged to plan, operate, and manage innovative transport systems. In an era characterized by the exponential growth of data availablity-enabled by on-boord sensors, mobile technologies ond digital platforms-students will gain the analytical skills and technical tools needed to analyze and interpret complex transport and mobility systems and, thus, make informed, data-driven decisions. The course focuses on integrating data analytics into transport engineering practices, covering both freight and passenger transport systems. Beginning with an overview of transport system typologies ond the fundamentals of data reliability and quality, the course introduces students te a braod range of quantitative methods ond practical tools, including: • mathematical optimization techniques for freight transport and logistics; • systems engineering approach for the design and management of transport networks; • machine learning and data mining methods, with practical opplications such as linear regression, clustering ond classification. To reinforce the learning process, the course includes group work and interactive hands-on classroom activities, aimed at developing analytical thinking, teamwork and problem-solving skills. By the end of the course, students will have acquired a data-oriented mindset, along with the ability to model, analyze, and support strategic and operational decisions in complex tronsport scenarios.
Transport systems and data analytics/Transport planning (Transport Planning)
Ability to analyze mobility data through basic statisticaI 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)
By the end of the course, students will hove acquired both solid theoretical foundations ond practical skills in data-driven transport engineering. Specifically, students will be able to: • demonstrate a clear understanding of the fundamental characteristics and dynamics of freight and passenger transport systems and their future trends; • analyse, plan, and optimise transport systems using quantitative methods, including optimization techniques supported by tools such as Lingo software; • evaluate and compare transport alternatives based on key performance indicators such as travel time, cast and environmental impact; • implement data mining ond machine leorning techniques to extract potterns from transport dota and enhance system performance; • criticalty assess the key challenges facing contemporary tronsport systems and anticipate their future developments in light of technological, environmental, and societal trends; • synthesise the outcomes of a work ond present them in front of on audience.
Transport systems and data analytics/Transport planning (Transport Planning)
Ability to analyze mobility data through basic statisticaI 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)
By the end of the course, students will hove acquired both solid theoretical foundations ond practical skills in data-driven transport engineering. Specifically, students will be able to: • demonstrate a clear understanding of the fundamental characteristics and dynamics of freight and passenger transport systems and their future trends; • analyse, plan, and optimise transport systems using quantitative methods, including optimization techniques supported by tools such as Lingo software; • evaluate and compare transport alternatives based on key performance indicators such as travel time, cast and environmental impact; • implement data mining ond machine leorning techniques to extract potterns from transport dota and enhance system performance; • criticalty assess the key challenges facing contemporary tronsport systems and anticipate their future developments in light of technological, environmental, and societal trends; • synthesise the outcomes of a work ond present them in front of on audience.
Transport systems and data analytics/Transport planning (Transport Planning)
Basic algebra and analysis (matrices, derivatives, integrals). Fundamentals of stotistics (probability distributions, cumulative distribution functions, mean, variance, standard error, coefficient of variation, confidence intervals, regression and least squares, the central limit theorem). Fundomentals of economics (demand and offer curves, project financial evoluation 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)
lt is recommended to have completed the course "Transport Economics and Technique". A solid foundation in engineering subjects such as Mathematics, Physics, end Chemistry is also required.
Transport systems and data analytics/Transport planning (Transport Planning)
Basic algebra and analysis (matrices, derivatives, integrals). Fundamentals of stotistics (probability distributions, cumulative distribution functions, mean, variance, standard error, coefficient of variation, confidence intervals, regression and least squares, the central limit theorem). Fundomentals of economics (demand and offer curves, project financial evoluation 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)
lt is recommended to have completed the course "Transport Economics and Technique". A solid foundation in engineering subjects such as Mathematics, Physics, end Chemistry is also required.
Transport systems and data analytics/Transport planning (Transport Planning)
1. lntroduction end surveying phase (9 hours of lessons end 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 end destinotion, trip productor and attractor. Representation of tronsport networks: concept of graph, centroid ond its localisation, centroid connector, O/D matrix, 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. lntroduction to the four-step model. • Trip generation. Classification of trips. Aggregate trip regression models for attractions (zoning tevel) and disaggregate trip regression models for productions (household or individuai level). Non linearity and aggregation of the results. Category analysis models. Balancing trip production and attraction models. • Trip distribution. Recall on generalised trip costs ond volue of traveI time. Singly and doubly constrained models. Growth factor methods. The gravity model. • Model 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. • Model split: disaggregate discrete choice models. Logistic regressìon and inear probability model, logit ond 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: random coefficients specification. • 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: all-or-nothing. Notes on the stochastic methods of multipath assignment. Capacity constrained assignment, Wardrop principles and Braess's poradox. 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 bosed on trips and zoning (EMME, Visum, TransCAD, Cube, OmniTrans) and on those based on agents and microsimulation (TRANSIMS, MATSim). 3. Building transport planning scenarios: evoluation and forecast (7 hours of lessons) • Evaluation phase of a given scenario. The dimensions of an evaluation process. An example of impacts quantification: emissions end dispersion models of pollutants. The monetization of the impacts: external and sociaI costs, travel time value and willingness to pay. • Forecost phase. Spatial and temperal transferability of demand and supply model parameters. Updating the model exogenous variables. Transport and land use, accessibility. lnduced traffic. Travel demand and mobility management tools: regulations, pricing, offer of new services. • Transport planning process and documents in ltaly. Planning of the national level: national transport plan, SIMPT. Planning at the regional and local level: regionol transport plan, urban mobility plan.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
1. lntroduction to transport systems and data analytics (1,5 hours) 2. Data-Driven Engineering in transport end logistics (6 hours) - Measurement and sampling techniques in transport - Data-driven applications, including motor vehicles, reconstructions of crashes, mobility management 3. Freight Transport Systems (4,5 hours) - Types of transport modes for freight: trend, features, transport means, stakeholders, pros and cons. - Model choice factors - lntermodal tronsport - Comparison between transport modes - ln-class exercises 4. Passenger Transport Systems (3 hours) - Types of transport modes for passengers: trend, features, transport means, stakeholders, pros and cons. - Mobility as o Service (MaaS) - Shared Mobility and technological evolution 5. Group Project 1: Comparative Evaluation of Transportation Alternatives (9 hours) Students will work in groups to apply theoretical concepts by comparing different transportation options in terms of cost, transit time, and environmental impact. Activities will be carried out using spreadsheets, online greenhouse gas (GHG) emissions calculators, ond presentation creation tools. Each group will prepare a final report and present their findings in front of the class. Both the written report and the oral presentation will contribute to the final course assessment. 6. Optimization techniques for Transportation (12 hours) - lntroduction to operations research and optimization techniques - Decision planning levels: strategic, tactical and operational - Location problems: theory and practical applications with Lingo software - Loading problems: theory and practical applications with Lingo software 7. Data Analytics and machine learning (4,5 hours) - Fundamentals of artificial intelligence: data analytics, data mining and machine learning - Types of machine learning algorithms - Machine Learning techniques, including regression methods, classification, and clustering 8. Graup Project 2: Optimization and Data analytics/Machine Learning application (10,5 hours) Students will work in groups to apply theoretical concepts related to optimization techniques, data analytics and machine learning. Activities will involve the use of LINGO, KNIME and/or Python software, as well as spreadsheets and presentation tools. Each group will prepare a final report and present their findings in front of the class. Both the written report and the oral presentation will contribute to the final course assessment. 9. Systems Engineering and Model-Bosed Systems Engineering in Transport (3 hours) - Fundamentals of Systems Engineering and Model-Based Systems Engineering - Application to transport systems using MATLAB-Simulink software 10. Technical Visit and/or Thematic Seminar (6 hours) Technical visit and/or seminar aligned with the course topics, aimed at consolidating theoretical knowledge through real-world exposure.
Transport systems and data analytics/Transport planning (Transport Planning)
1. lntroduction end surveying phase (9 hours of lessons end 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 end destinotion, trip productor and attractor. Representation of tronsport networks: concept of graph, centroid ond its localisation, centroid connector, O/D matrix, 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. lntroduction to the four-step model. • Trip generation. Classification of trips. Aggregate trip regression models for attractions (zoning tevel) and disaggregate trip regression models for productions (household or individuai level). Non linearity and aggregation of the results. Category analysis models. Balancing trip production and attraction models. • Trip distribution. Recall on generalised trip costs ond volue of traveI time. Singly and doubly constrained models. Growth factor methods. The gravity model. • Model 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. • Model split: disaggregate discrete choice models. Logistic regressìon and inear probability model, logit ond 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: random coefficients specification. • 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: all-or-nothing. Notes on the stochastic methods of multipath assignment. Capacity constrained assignment, Wardrop principles and Braess's poradox. 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 bosed on trips and zoning (EMME, Visum, TransCAD, Cube, OmniTrans) and on those based on agents and microsimulation (TRANSIMS, MATSim). 3. Building transport planning scenarios: evoluation and forecast (7 hours of lessons) • Evaluation phase of a given scenario. The dimensions of an evaluation process. An example of impacts quantification: emissions end dispersion models of pollutants. The monetization of the impacts: external and sociaI costs, travel time value and willingness to pay. • Forecost phase. Spatial and temperal transferability of demand and supply model parameters. Updating the model exogenous variables. Transport and land use, accessibility. lnduced traffic. Travel demand and mobility management tools: regulations, pricing, offer of new services. • Transport planning process and documents in ltaly. Planning of the national level: national transport plan, SIMPT. Planning at the regional and local level: regionol transport plan, urban mobility plan.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
1. lntroduction to transport systems and data analytics (1,5 hours) 2. Data-Driven Engineering in transport end logistics (6 hours) - Measurement and sampling techniques in transport - Data-driven applications, including motor vehicles, reconstructions of crashes, mobility management 3. Freight Transport Systems (4,5 hours) - Types of transport modes for freight: trend, features, transport means, stakeholders, pros and cons. - Model choice factors - lntermodal tronsport - Comparison between transport modes - ln-class exercises 4. Passenger Transport Systems (3 hours) - Types of transport modes for passengers: trend, features, transport means, stakeholders, pros and cons. - Mobility as o Service (MaaS) - Shared Mobility and technological evolution 5. Group Project 1: Comparative Evaluation of Transportation Alternatives (9 hours) Students will work in groups to apply theoretical concepts by comparing different transportation options in terms of cost, transit time, and environmental impact. Activities will be carried out using spreadsheets, online greenhouse gas (GHG) emissions calculators, ond presentation creation tools. Each group will prepare a final report and present their findings in front of the class. Both the written report and the oral presentation will contribute to the final course assessment. 6. Optimization techniques for Transportation (12 hours) - lntroduction to operations research and optimization techniques - Decision planning levels: strategic, tactical and operational - Location problems: theory and practical applications with Lingo software - Loading problems: theory and practical applications with Lingo software 7. Data Analytics and machine learning (4,5 hours) - Fundamentals of artificial intelligence: data analytics, data mining and machine learning - Types of machine learning algorithms - Machine Learning techniques, including regression methods, classification, and clustering 8. Graup Project 2: Optimization and Data analytics/Machine Learning application (10,5 hours) Students will work in groups to apply theoretical concepts related to optimization techniques, data analytics and machine learning. Activities will involve the use of LINGO, KNIME and/or Python software, as well as spreadsheets and presentation tools. Each group will prepare a final report and present their findings in front of the class. Both the written report and the oral presentation will contribute to the final course assessment. 9. Systems Engineering and Model-Bosed Systems Engineering in Transport (3 hours) - Fundamentals of Systems Engineering and Model-Based Systems Engineering - Application to transport systems using MATLAB-Simulink software 10. Technical Visit and/or Thematic Seminar (6 hours) Technical visit and/or seminar aligned with the course topics, aimed at consolidating theoretical knowledge through real-world exposure.
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)
Eight workshops will be held during the course; each workshop will deal with a particular task in the transport planning process, but all workshops will be based on the same cose 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. 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 combines a voriety of complementary teaching methods aimed at fostering both theoretical understanding and practical application: • Theoreticol lectures: These sessions provide the foundational concepts, analytical frameworks, and methodological tools related to transport systems and data analytics. • Practical exercises: Hands-on activities and problem-solving sessions allow students to apply theoretical concepts using real or simulated data. Exercises include data analysis and use of specific softwares (e.g., LINGO, KNIME, Phyton, Matlab). • Group projects: Students will complete two group assignments, each focusing on a specific topic covered in the course (e.g., optimization of a transport system, data analytics and machine learning). Each project will culminate in a presentation in front ol the class, encouraging public speaking skills, critical discussion, and peer learning. • Technicol visit: A field visit to a transport facility (e.g., a logistics hub, terminal, or control center) will be organized to give students direct exposure to operational practices and data-driven decision-making in the transport sector. • Guest seminar and company presentation: An industry expert will be invited to deliver a seminar illustrating current trends, tools and challenges in transport data analytics. The session will include a presentation from a company representative, highlighting prafessional applications and career insights. • lntermediary evaluation: An interim assessment (such as a quiz, written test, or assignment) will be conducted during the course to monitor students' progress, reinforce key concepts, and provide formative feedback ahead of the final evaluation.
Transport systems and data analytics/Transport planning (Transport Planning)
Eight workshops will be held during the course; each workshop will deal with a particular task in the transport planning process, but all workshops will be based on the same cose 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. 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 combines a voriety of complementary teaching methods aimed at fostering both theoretical understanding and practical application: • Theoreticol lectures: These sessions provide the foundational concepts, analytical frameworks, and methodological tools related to transport systems and data analytics. • Practical exercises: Hands-on activities and problem-solving sessions allow students to apply theoretical concepts using real or simulated data. Exercises include data analysis and use of specific softwares (e.g., LINGO, KNIME, Phyton, Matlab). • Group projects: Students will complete two group assignments, each focusing on a specific topic covered in the course (e.g., optimization of a transport system, data analytics and machine learning). Each project will culminate in a presentation in front ol the class, encouraging public speaking skills, critical discussion, and peer learning. • Technicol visit: A field visit to a transport facility (e.g., a logistics hub, terminal, or control center) will be organized to give students direct exposure to operational practices and data-driven decision-making in the transport sector. • Guest seminar and company presentation: An industry expert will be invited to deliver a seminar illustrating current trends, tools and challenges in transport data analytics. The session will include a presentation from a company representative, highlighting prafessional applications and career insights. • lntermediary evaluation: An interim assessment (such as a quiz, written test, or assignment) will be conducted during the course to monitor students' progress, reinforce key concepts, and provide formative feedback ahead of the final evaluation.
Transport systems and data analytics/Transport planning (Transport Planning)
The reference book is Ortuzar, J.d.D. and Willumsen, LG. (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 will be distributed by the instructor through the course webpage.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Teaching Materials: - lecture Handouts: materials prepared by the lecturer covering course topics will be distributed during the lessons. Main References: 1. Teodorovic D. and Milan Janié M. (2016), Transportation Engineering Theory, Practice and Modeling, Butterworth-Heinemann. 2. Dalla Chiara, B. (2021) ITS tor Transport Planning and Policy. In Vickerrnan, R. (Ed.), lnternational Encyclopedia of Transportation, Vol. 6, pp. 298-308. Elsevier Ltd, United Kingdom. 3. Ghiani, G., Laporte, G., & Musmanno, R. (2013). lntroduction to Logistics Systems Management. John Wiley & Sons 4. Vickerman, R. (Ed.). (2021). lnternational Encyclopedia of Transportation. Esevier Ltd, United Kingdom. (sections related to data analytics) 5. Williams, H. P. (2013). Model Building in Mathematical Programming. John Wiley & Sons. 6. Dayyola, N., Kottayi, N. M., & Mallick, R. B. (2024). Machine Learning in Transportation: Applications with Examples and Codes. Walter de Gruyter GmbH & Co KG. 7. Yan, R., & Wang, S. (2022). Applications of machine learning and data analytics models in maritime transportation. lnstitution of Engineering and Technology.
Transport systems and data analytics/Transport planning (Transport Planning)
The reference book is Ortuzar, J.d.D. and Willumsen, LG. (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 will be distributed by the instructor through the course webpage.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
Teaching Materials: - lecture Handouts: materials prepared by the lecturer covering course topics will be distributed during the lessons. Main References: 1. Teodorovic D. and Milan Janié M. (2016), Transportation Engineering Theory, Practice and Modeling, Butterworth-Heinemann. 2. Dalla Chiara, B. (2021) ITS tor Transport Planning and Policy. In Vickerrnan, R. (Ed.), lnternational Encyclopedia of Transportation, Vol. 6, pp. 298-308. Elsevier Ltd, United Kingdom. 3. Ghiani, G., Laporte, G., & Musmanno, R. (2013). lntroduction to Logistics Systems Management. John Wiley & Sons 4. Vickerman, R. (Ed.). (2021). lnternational Encyclopedia of Transportation. Esevier Ltd, United Kingdom. (sections related to data analytics) 5. Williams, H. P. (2013). Model Building in Mathematical Programming. John Wiley & Sons. 6. Dayyola, N., Kottayi, N. M., & Mallick, R. B. (2024). Machine Learning in Transportation: Applications with Examples and Codes. Walter de Gruyter GmbH & Co KG. 7. Yan, R., & Wang, S. (2022). Applications of machine learning and data analytics models in maritime transportation. lnstitution of Engineering and Technology.
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; 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; 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; Accertamento (esame senza voto);
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; Check;
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). The final evoluation keeps into account both the quality of the work done during the course and the performance during the final oraI exam.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
The final grade (maximum 30 cum laude) is determined as follows: • Oral examination held at the end of the course, covering the full syllabus. Maximum score: 24 points • Two group assignments carried out during the course: - Assignment 1 contributes up to 3 points - Assignment 2 contributes up to 4 points far a combined total of 7 additional points An intermediate assessment will be carried out during the semester; while it does not affect the final grade, it serves as a means to monitor students' progress and support ongoing learning. Registration via the university teaching portai is mandatory. Students who are unable to attend the oral exam must cancel their booking in advance.
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; Check;
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). The final evoluation keeps into account both the quality of the work done during the course and the performance during the final oraI exam.
Transport systems and data analytics/Transport planning (Transport systems and data analytics)
The final grade (maximum 30 cum laude) is determined as follows: • Oral examination held at the end of the course, covering the full syllabus. Maximum score: 24 points • Two group assignments carried out during the course: - Assignment 1 contributes up to 3 points - Assignment 2 contributes up to 4 points far a combined total of 7 additional points An intermediate assessment will be carried out during the semester; while it does not affect the final grade, it serves as a means to monitor students' progress and support ongoing learning. Registration via the university teaching portai is mandatory. Students who are unable to attend the oral exam must cancel their booking in advance.