Can data fusion improve traffic management?
Traffic management is an important tool to relieve congestion problems. For traffic management knowledge about the current traffic situation is essential. Normally, the data that is used to keep traffic operators informed about the current traffic situations, comes from one or more sources, but is not integrated. For example: camera's send their images to the control centre and loop detectors generate data about flows and speeds, but they are shown and used in different ways. The question now is if it is possible to integrate data from different sources (data fusion) in such a way that a better and more complete picture of the current traffic situation is obtained.
Finished reports
- Performance of Rush-hour and Plus Lanes - A study to behavioral and design factors
- Using speed limits to prevent congestion at fixed infrastructural bottlenecks
- Short-term traffic prediction in road traffic control centres
- Route Guidance and Signal Control Based on the Back-Pressure Algorithm
- Robust model-based optimization of evacuation guidance
- Design of a Subnetwork Controller Based on MFD’s and Perimeter Flows
- Multi-objective Traffic Management for Livability
- Onderzoek naar de identificatie van kwetsbare wegvakken
- The influence of dynamic route guidance systems on traffic management
- Concept Design and Evaluation of Traffic Management in Beijing
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Projects in progress
Finished projects
- Dynamic OD matrix estimation using floating car data
- Possibilities of video monitoring
- Incident detection with loop data
- FastLane: modelling and simulation of traffic flow
- Alternative routes A15 Maasvlakte - Vaanplein
- Bottlenecks in road networks due to incidents
- Data fusion of loop detection and travel time measurements
- The relation between traffic flow and lane width
- Real-time capacity estimation for adaptive ramp metering
- Traffic flow and safety during incidents