Most traffic flow control algorithms address switching cycle adaptation of
traffic signals and lights. This work addresses traffic flow optimisation by
self-organising micro-level control combining Reinforcement Learning and
rule-based agents for action selection performing long-range navigation in
urban environments. I.e., vehicles represented by agents adapt their decision
making for re-routing based on local environmental sensors. Agent-based
modelling and simulation is used to study emergence effects on urban city
traffic flows. An unified agent programming model enables simulation and
distributed data processing with possible incorporation of crowd sensing tasks
used as an additional sensor data base. Results from an agent-based simulation
of an artificial urban area show that the deployment of micro-level vehicle
navigation control just by learned individual decision making and re-routing
based on local environmental sensors can increase the efficiency of mobility in
terms of path length and travelling time.

本研究基于自组织微级控制与强化学习及基于规则的行动选择，利用代理模型和模拟来研究城市交通流的紧密关联和效率提升，通过环境传感器的本地感知进行个体决策和改变路线，以增加行程的效率，缩短道路长度和旅行时间。