Sebastian Senge

According to our database1, Sebastian Senge authored at least 12 papers between 2007 and 2014.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of five.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2014
Ein Bienen-inspiriertes Schwarmintelligenz-Verfahren zum Routing im Straßenverkehr.
PhD thesis, 2014

2013
BeeJamA: A Distributed, Self-Adaptive Vehicle Routing Guidance Approach.
IEEE Trans. Intell. Transp. Syst., 2013

Assessment of path reservation in distributed real-time vehicle guidance.
Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), 2013

Marginal cost pricing and multi-criteria routing in a distributed swarm-intelligence approach for online vehicle guidance.
Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, 2013

2012
Minimizing Vehicular Travel Times Using the Multi-Agent System BeeJamA.
Proceedings of the Product-Focused Software Process Improvement, 2012

2-Way evaluation of the distributed BeeJamA vehicle routing approach.
Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, 2012

Bee-Inpired Road Traffic Control as an Example of Swarm Intelligence in Cyber-Physical Systems.
Proceedings of the 38th Euromicro Conference on Software Engineering and Advanced Applications, 2012

2011
Towards hybrid simulation of self-organizing and distributed vehicle routing in large traffic systems.
Proceedings of the 4th International Conference on Biomedical Engineering and Informatics, 2011

2009
Bee Inspired Bottom-Up Self-Organization in Vehicular Traffic Management.
Proceedings of the Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2009

Bienen-inspiriertes Straßenverkehrsrouting.
Proceedings of the Software-intensive verteilte Echtzeitsysteme, 2009

2007
Highly Dynamic and Adaptive Traffic Congestion Avoidance in Real-Time Inspired by Honey Bee Behavior.
Proceedings of the Mobilität und Echtzeit, 2007

A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior.
Proceedings of 12th IEEE International Conference on Emerging Technologies and Factory Automation, 2007


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