TNK120 · 6 credits · Advanced level (A1N) · Transportsystem
Traffic State Estimation, Prediction and Control
Active · next start HT 2026
100%
passed the latest regular sitting
LAB2 · 26 Nov 2025 · 18 students
100%
passed the regular sittings, all sittings over the last 5 years
LAB2 · 32 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 2 programmes
Hand-in or lab
LAB2 Laboratory Work
- G
Main rounds
100% passed · 32 students
- 100%18
- 100%14
Later rounds
100% passed · 12 students
- 100%7
- 100%5
- Pass a later round: 100% (12 students), compared with 100% (32 students) on the ordinary exam
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 100%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Individual Assignments
- 5
- 4
- 3
- U
Main rounds
100% passed · 27 students
- 100%14
- 100%13
Later rounds
92% passed · 12 students
- 86%7
- 100%5
- Pass a later round: 92% (12 students), compared with 100% (27 students) on the ordinary exam
- Get 4 or 5: 26% (39 students)
Show small sittings (12)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%3
- 100%1
- 100%1
- 100%1
- 100%1
- 100%1
- 100%2
- 100%1
- 100%1
- 100%2
- 100%1
- 100%1
About the course
Aim
Traffic state estimation and prediction are essential components of traffic information and control systems. The purpose of this course is to provide basic knowledge in the area of traffic estimation, prediction and control, and give insight on how traffic control can be used for improving the efficiency of traffic systems and reducing the environmental impact from car traffic in urban areas. Furthermore will the course introduce dynamic network wide modeling of traffic, and give insight in the underlying theory of dynamic user equilibrium models.
After the course, the student shall be able to:
- Describe and utilize filtering methods on heterogeneous traffic data, for traffic state estimation purposes
- Describe and utilize basic data driven method for traffic state prediction
- Formulate and utilize macroscopic traffic flow simulation models for traffic analysis
- Describe and apply traffic control strategies for motorways
- Describe and utilize dynamic traffic assignment models for evaluating and analyzing traffic management and control systems
- Describe and utilize methods for evaluating traffic impact on the environment
- Describe different types of traffic models’ advantages, disadvantages, and suitability for analyzing different types of traffic facilities and traffic control and management systems for urban networks
- Discuss and analyze the relevance of the course and the course content in relation to the scope of the master program that the student are taking and in relation to a future working career as a traffic engineer
Content
- Introduction to traffic state estimation
- Filtering and assimilation of heterogeneous traffic data
- Introduction to data driven traffic state prediction
- Macroscopic traffic simulation
- Traffic control on motorways
- Introduction to dynamic traffic network assignment and mesoscopic traffic simulation
- Experiments with dynamic network assignment models
- Application of dynamic traffic assignment methods to traffic management problems
- Emission modeling
- Examiner
- David Gundlegård
- Scheduled hours
- 44 h
- Main field of study
- Transportsystem