342-0960/01 – Advanced Methods of Transport Prognoses (PMDP)

Gurantor departmentInstitute of TransportCredits10
Subject guarantordoc. Ing. Michal Dorda, Ph.D.Subject version guarantordoc. Ing. Michal Dorda, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFSIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
DOR028 doc. Ing. Michal Dorda, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 25+0
Part-time Examination 25+0

Subject aims expressed by acquired skills and competences

The student will be able to recognize individual types of tasks that can be encountered when solving practical problems in transport forecasting. Based on the analysis of the problem solved, he will be able to choose a suitable method or group of methods (time series analysis methods, regression and correlation analysis, statistical hypothesis testing, gravity models, etc.). It will be able to determine what data and the extent to which it will need to be solved. Last but not least, he will be able to solve the problem by using a suitable software tool.

Teaching methods

Lectures
Individual consultations
Project work

Summary

The graduate will master the problems of general classical prognostic models based on mathematical statistics (methods of descriptive statistics, statistical theory of estimation, testing of statistical hypotheses, regression and correlation analysis and time series analysis) as well as problems of prognostic models based on unconventional approaches (methods of operational analysis, neural networks). From the field of special prognostic models designed for the needs of transport systems, attention will be focused on modern trends in the development of gravitational models, methods of specific momentum etc.

Compulsory literature:

ORTÚZAR SALAS, Juan de Dios a G WILLUMSEN, Luis. Modelling transport. 4th ed. Chichester: John Wiley, c2011. ISBN 978-0-470-76039-0.

Recommended literature:

HENSHER, David A.; BUTTON, Kenneth J. (ed.). Handbook of transport modelling. Emerald Group Publishing Limited, 2007.

Way of continuous check of knowledge in the course of semester

Oral examination.

E-learning

Other requirements

Solution and defense of the project on the given topic.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1) Descriptive statistics methods. 2) Statistical theory of point and interval estimates. 3) Statistical hypothesis tests - dependence of variables, tests of good agreement. 4) Regression and correlation analysis methods for traffic forecasting. 5) Analysis and modeling of time series and their use for traffic forecasting. 6) Trip generation methods - Multiple regression, momentum methods, etc. 7) Methods of O / D matrix creation - methods based on growth factors, gravitational models, etc. 8) Choice of mode of transport - methods of utility. 9) Assignment to the transport network. 10) Unconventional approaches to transport prediction - Bayesian networks, hidden Markov models, Kalman filters, etc. 11) Reliability of forecasts.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P1041D040006) Transport Systems P Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P1041D040006) Transport Systems K Czech Ostrava Choice-compulsory type B study plan
2020/2021 (P1041D040006) Transport Systems P Czech Ostrava Choice-compulsory type B study plan
2020/2021 (P1041D040006) Transport Systems K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner