After this course, the student is able to:|
Apply and test different approaches of network modelling, with emphasis on the aggregated 4 (5) stage model including:
- trip generation,
- trip distribution,
- mode choice,
- time of day/departure time choice,
- traffic assignment
Prepare traffic data and describe different types of variation in the traffic data that enables the student to:
estimate errors in traffic data
calibrate the network models
evaluate and assess network model forecasts
An important aspect of traffic engineering is forecasting of network performance under different future policy and technological scenarios. This course is about the actual modelling techniques that are used, about the definition of assessment criteria, and about assessing different future scenarios using network models.|
It includes the description of different model types, the theory of travel behavior, theory of choice models, aggregate and disaggregate models, mode choice, route choice and assignment modelling. Calibration of parameters and model validation are included, using observations, error estimation and validation methods.
Main topics are the 4 stage transport model and model calibration using traffic data (i.e., traffic counts and Floating Car Data). Emphasis is on macroscopic methods to be able to model the dynamics of traffic operations more realistically, and to evaluate them properly based on traffic data. Additionally, aggregate and non-aggregate performance measures are part of this course, including techniques to derive these from the network simulation model.
This course is assessed by means of a written exam and a group assignment. A satisfactory completion of the course requires an overall mark of at least 5.5, and a written exam mark of at least 5.0.