Métodos Quantitativos de Análise em Transportes

Objectives

1. The main objective is to train future transport engineers to master the concepts, models and quantitative methods that are fundamental to supporting transport planning. 2. With a pedagogical approach of practical application, students will become familiar with calculation tools for statistical characterization, synthesis and inference. 3. Emphasis is placed on multivariate models to explain the relationships between observed data as well as to make estimates and forecasts. 4. The description, explanation and application of the various tools will be followed by a critical analysis of the scope of application and limitations. 5. Students should be able to analyse and convey the results in a simple and assertive manner.

Syllabus

1. Database construction and analysis 2. Multi-variant statistical analysis: objectives, potentialities and fields of application 3. Descriptive statistics: revision of basic concepts 4. Introduction to the statistical package R 5. Multivariate statistical models: the multivariate analysis perspective 6. Dependence models: multiple regression and correlation 7. Summarization models and data structure identification: principal components and factorial analysis 8. Classification models: cluster analysis 9. Introduction to Geographic Information Systems (GIS): concept, potentialities, GIS Models, Geographic Information for GIS-T, Network Analysis 10. Introduction to transport system modelling

Prerequisites

For admitted students to the Master of Science of Transportation Systems, there are no additional requirements. For other students, a completed bachelor's degree in any engineering course at IST, or urban planning, geography, economics, with at least 24 ECTS in mathematics, physics, statistics and/or computer science.

Cross Competence Component

The UC allows the development of soft skills in Critical Thinking, namely strategic thinking and problem-solving approaches, during the project development; and Interpersonal and intrapersonal skills (oral, organizational and team work, self-discipline, perseverance, self-motivation) also during the project development. The assessment percentage associated with these competences should be around 20%.

Laboratorial Component

MQAT will have a computer lab component in which students will have an introduction to the R statistics package, through an application to database case studies provided by the faculty. Similarly, students will have an introduction to the use of ArcGIS.