C4.5: programs for machine learning
C4.5: programs for machine learning
Traffic Prediction for Agent Route Planning
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Integrating Data Mining and Agent Based Modeling and Simulation
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
To adapt or not to adapt: consequences of adapting driver and traffic light agents
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
From GIS to mixed traffic simulation in urban scenarios
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Change point analysis for intelligent agents in city traffic
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Selfish road users --- case studies on rule breaking agents for traffic simulation
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
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The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.