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.
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.