Towards Truly Agent-Based Traffic and Mobility Simulations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Adaptation in games with many co-evolving agents
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
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
Road traffic optimisation using an evolutionary game
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Simulation of coordinated anticipatory vehicle routing strategies on MATSim
PRIMA'11 Proceedings of the 14th international conference on Agent Based Simulation for a Sustainable Society and Multi-agent Smart Computing
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Human drivers may perform replanning when facing traffic jams or when informed that there are expected delays on their planned routes. In this paper, we address the effects of drivers re-routing, an issue that has been ignored so far. We tackle re-routing scenarios, also considering traffic lights that are adaptive, in order to test whether such a form of co-adaptation may result in interferences or positive cumulative effects. An abstract route choice scenario is used which resembles many features of real world networks. Results of our experiments show that re-routing indeed pays off from a global perspective as the overall load of the network is balanced. Besides, re-routing is useful to compensate an eventual lack of adaptivity regarding traffic management.