Quantitative Methods in Transportation

Prerequisite Knowledge

Basic transport and optimization tools taught in the courses, Introduction to Systems Optimization (KEY (Obligatory Choice of One) - 5th Semester) and Transport Systems Planning (Compulsory - 6th Semester). Knowledge of basic concepts of statistical analysis is also a prerequisite.

Course Units

#	Title	Description	Hours
1	Intelligent Transport Systems	Introduction. Systems. Processes and Patterns. Databases. Introduction to Quantitative Methods.	2Χ4=8
2	Applied Statistical Modelling	Databases Analysis and Hypothesis Testing. Linear and Logistic regression, methods of stated and revealed preference. Time series Analysis.	4Χ4=16
3	Optimization	Transport and allocation problems. Optimal design problems. Advanced traffic assignment methods and Public Transport. Intelligent Transport Systems and Optimization.	3Χ4=12
4	Programming in R	Introduction to R. Basic Programming Concepts. Classification, clustering, optimization and forecasting techniques in transportation using R.	4Χ4=16

Learning Objectives

Upon successful completion of the course, students will be able to:

1. know the basic categories of quantitative methods they can apply to transport problems,
2. understand the influence of quantitative methods on modern intelligent transport systems,
3. understand the importance of open source software and programming in dealing with transport problems,
4. develop programming code for the implementation of models in order to solve transport problems, and
5. evaluate these models in terms of their usefulness and credibility.

Teaching Methods

Teaching methods	Lectures in class. Development of exemplary implementations of coding in the classroom. Execution and delivery of four individual exercises.
Teaching media	Presentations on the blackboard. Computing using R.
Computer and software use	Students solve exemplary exercises in the classroom with the guidance of the tutors using R. The prerequisite exercises are treated and delivered through the programming environment R.
Problems - Applications	There are exemplary solved applications related to model developing, which are thoroughly explained in the classroom during the lectures.
Assignments (projects, reports)	Students perform and deliver 4 preset exercises individually, using programming environment R. The assignments are corrected by the tutors and are returned during the oral exam.

Student Assessment

• Assignments (projects, reports): 100%

Textbooks - Bibliography

Textbooks

Σταθόπουλος, Α. , Καρλαύτης, Μ. (2008). Σχεδιασμός Μεταφορικών Συστημάτων, Εκδόσεις Παπασωτηρίου, Αθήνα. Α. Σταθόπουλος "Eιδικά Θέματα Μεταφορών - Σημειώσεις κατά την παράδοση", ΕΜΠ, Αθήνα 2005

Recommended Bibliography

Advanced Methods in Transport Systems Planning Ortuzar, J.D., Willumsen, L.G. (2011). Modelling Transport, 4th Edition. Willey, New York Washington, S. P., Karlaftis, M. G., & Mannering, F. L. (2010). Statistical and econometric methods for transportation data analysis. CRC press. Cascetta, E. (2009). Transportation Systems AnalysisModels and Applications, Springer, Berlin. Φραντζεσκάκης, Ι. Μ., Γκόλιας, Ι. Κ., Πιτσιάβα-Λατινοπούλου, Μ. Χ. (2008). Κυκλοφοριακή Τεχνική, Εκδόσεις Παπασωτηρίου, Αθήνα. Hensher, D.A., Button, K..J. (2000) Handbook of Transport Modelling. Elsevier. New York

Optimization Methods Chong, E. K., & Zak, S. H. (2013). An introduction to optimization (Vol. 76). John Wiley & Sons. Καρλαύτης, Μ. Λαγαρός, Ν. Δ. (2010). Επιχειρησιακή έρευνα και βελτιστοποίηση για μηχανικούς, Συμμετρία, Αθήνα. Rao, S. S., & Rao, S. S. (2009). Engineering optimization: theory and practice. John Wiley & Sons. Antoniou, A., & Lu, W. S. (2007). Practical optimization: algorithms and engineering applications. Springer Science & Business Media. Winston, W (2003), Operations Research: Applications and Algorithms, Cengage Learning.

Applied Statistical Modelling Verzani, J. (2014). Using R for introductory statistics. CRC Press. Washington, S. P., Karlaftis, M. G., & Mannering, F. L. (2010). Statistical and econometric methods for transportation data analysis. CRC press. Navidi, W. C. (2008). Statistics for engineers and scientists. McGraw-Hill Higher Education. Joaquim, P., & Marques, S. (2007). Applied statistics using SPSS, statistica, Matlab and R. Springer Company USA, 205-211. Ζιούτας Γ. Χ. (2003). Πιθανότητες και Στοιχεία Στατιστικής για Μηχανικούς, εκδόσεις Ζήτη, Θεσσαλονίκη.

Computational Intelligence and Machine Learning Marsland, S. (2014). Machine learning: an algorithmic perspective. CRC press. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013). Computational intelligence: a methodological introduction. Springer Science & Business Media. Karlaftis, M. G. and Vlahogianni, E. I. (2011). Statistics versus Neural Networks in Transportation Research: Differences, Similarities and Some Insights, Transportation Research Part C: Emerging Technologies, 19(3), 387-399. Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley & Sons. TRB (2007). Artificial Intelligence in Transportation: Information for Application, Transportation Research Circular E-C113, Transportation Research Board, Washington DC. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Forecasting in Transportation Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: principles and practice. OTexts. Vlahogianni, E I., Karlaftis, M. G., Golias, J.C. (2014). Short-term Traffic Forecasting: Where We Are and Where We're Going. Transportation Research Part C: Emerging Technologies, 43(1), 3-19. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1). Springer, Berlin: Springer series in statistics. Chambers, D., & Mandic, J. (2001). Recurrent neural networks for prediction: learning algorithms architecture and stability. John Wiley & Sons, Ltd., Chichester, 18, 32.

Lecture Time - Place:

• Wednesday, 13:45 – 17:30,
  Rooms:
  • Αμφ. Σιδ/κής