342-0960/02 – Advanced Methods of Transport Prognoses (PMDP)
| Gurantor department | Institute of Transport | Credits | 10 |
| Subject guarantor | doc. Ing. Michal Dorda, Ph.D. | Subject version guarantor | doc. Ing. Michal Dorda, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FS | Intended for study types | Doctoral |
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:
Recommended literature:
HENSHER, David A.; BUTTON, Kenneth J. (ed.). Handbook of transport modelling. Emerald Group Publishing Limited, 2007.
Additional study materials
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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