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
- Dynamic OD matrix estimation using floating car data
- An Assessment Framework for the Speed Policy on Dutch Motorways
- Congestion minimisation by optimising merging behaviour through Intelligent Transportation Systems
- Performance of Rush-hour and Plus Lanes - A study to behavioral and design factors
- Allocating Departure Time Slot to Optimize Dynamic Network Capacity
- Design of an urban traffic controller that incorporates route advice given by in-vehicle navigation systems
- A-Priori Travel Time Predictor for Long Term Roadworks on Motorways
- HOT lanes - Implementation considerations and assessment
- Evaluating Multi-Class Model Predictive Control
- Verkeersinformatie vanuit de NDW
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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