| Course Unit Code | 342-0577/05 |
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| Number of ECTS Credits Allocated | 3 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| DOR028 | doc. Ing. Michal Dorda, Ph.D. |
| Summary |
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| 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. |
| Learning Outcomes of the Course Unit |
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| 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. |
| Course Contents |
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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. |
| Recommended or Required Reading |
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| Required Reading: |
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Briš,R.-Škňouřilová,P.:Statistics I. VŠB-TU Ostrava, 2007.
Hill,T.-Lewicki,P.: Statistics : methods and applications : a comprehensive reference for science, insdustry and data mining. StatSoft, Tulsa, 2006, 832 pp. ISBN 1-884233-59-7. |
Křivda, V. Metody dopravního prognózování I. Ostrava: VŠB - Technická univerzita Ostrava, 2009, 179 s. ISBN 978-80-248-2121-4.
Seger, J.; Hindls, R.: Statistické metody v tržním hospodářství. Victoria publishing, Praha, 1995, 435 s. ISBN 80-7187-058-7.
Medelská, V., Jirava, P., Nop, D., Rojan, J. Dopravné inžinierstvo. 1. vyd. Alfa Bratislava, 1991, 376 s. ISBN 80-05-00737-X.
Hill,T.-Lewicki,P.: Statistics : methods and applications : a comprehensive reference for science, insdustry and data mining. StatSoft, Tulsa, 2006, 832 pp. ISBN 1-884233-59-7. |
| Recommended Reading: |
|---|
Briš,R.-Škňouřilová,P.:Statistics I. VŠB-TU Ostrava, 2007.
Hill,T.-Lewicki,P.: Statistics : methods and applications : a comprehensive reference for science, insdustry and data mining. StatSoft, Tulsa, 2006, 832 pp. ISBN 1-884233-59-7. |
Folprecht, J.; Křivda, V. Organizace a řízení dopravy I. Ostrava: VŠB - Technická univerzita Ostrava, 2006, 158 s. ISBN 80-248-1030-1.
Křivda, V. Základy organizace a řízení silniční dopravy. Ostrava: VŠB - Technická univerzita Ostrava, 2006, 170 s. ISBN 80-248-1253-3. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |