342-6510/01 – Methods of Logistics System Forecasting (MPLS)

Gurantor departmentInstitute of TransportCredits6
Subject guarantordoc. Ing. Michal Dorda, Ph.D.Subject version guarantordoc. Ing. Michal Dorda, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory type B
Year1Semestersummer
Study languageCzech
Year of introduction2021/2022Year of cancellation
Intended for the facultiesFSIntended for study typesFollow-up Master
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 Credit and Examination 2+3
Part-time Credit and Examination 6+16

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 in transport and logistics. 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:

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.

Recommended literature:

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.

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. Exam - written and oral part.

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, traffic surveys and their classification. 2. Traffic survey of intensities according to TP 189. 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

Part-time form (validity from: 2023/2024 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 35  20
        Examination Examination 65  31 3
Mandatory attendence participation: 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 at the end of the semester. Combined exam - written and oral part.

Show history

Conditions for subject completion and attendance at the exercises within ISP: Attendance in lessons is not required for students with ISP, if necessary, the possibility of conducting individual consultations. 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 at the end of the semester. Combined exam - written and oral part.

Show history

Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N1041A040013) Intelligent transport and logistics P Czech Ostrava 1 Choice-compulsory type B study plan
2024/2025 (N1041A040013) Intelligent transport and logistics K Czech Ostrava 1 Choice-compulsory type B study plan
2023/2024 (N1041A040013) Intelligent transport and logistics P Czech Ostrava 1 Choice-compulsory type B study plan
2023/2024 (N1041A040013) Intelligent transport and logistics K Czech Ostrava 1 Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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