342-0577/05 – Methods of Transport Prognostics (MDP)
| Gurantor department | Institute of Transport | Credits | 3 |
| Subject guarantor | doc. Ing. Michal Dorda, Ph.D. | Subject version guarantor | doc. Ing. Michal Dorda, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2012/2013 | Year of cancellation | |
| Intended for the faculties | FS | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The student will get acquainted with selected types of traffic surveys and their evaluation. We will also get acquainted with the issue of measuring dependencies, ie regression and correlation analysis, analysis of time series suitable for forecasting. He will be able to apply these methods to practical tasks.
Teaching methods
Lectures
Tutorials
Summary
The course deals with general prognostic methods and methods specialized for transport. Attention is paid to methods of processing random vectors and the issue of dependencies between its components. The course also includes issues of regression and correlation analysis, time series analysis and other suitable tools.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Individual work of students:
• Task 1 - use of multiple regression analysis in forecasting prospective traffic volumes.
• Task 2 - use of gravity model in determining interregional relationships.
Credit test in the middle and at the end of the semester.
E-learning
lms.vsb.cz
Other requirements
No other requirements are defined.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to traffic forecasting, exploratory data analysis - introduction, measures of central tendency.
2. Exploratory data analysis - measures of dispersion, outlier identification, large random sample processing.
3. Random vector and its description, covariance, simple correlation coefficient.
4. Independence testing in the combination table.
5. Introduction to time series - basic concepts, division of time series.
6. Time series trend analysis - linear trend, parabolic trend, exponential trend.
7. Time series trend analysis - modified exponential trend, logistic curve, Gompertz curve.
8. Correlation analysis - Pearson's correlation coefficient, linear independence testing.
9. Regression analysis - basic concepts, types of regression functions, least squares method and its application in estimating the parameters of a regression function, multiple regression.
10. Four-phase model of traffic forecast - calculation of prospective traffic volumes - use of regression analysis, methods of specific momentum.
11. Four-phase model of traffic forecast - determination of interregional relations - analogous methods.
12. Four-phase model of traffic forecast - determination of interregional relations - synthetic methods.
13. Four-phase model of traffic forecast - division of transport work, allocation to the network.
14. Reserve.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction