论文标题
使用强化学习和基于代理的建模对微观层面的自组织城市交通控制
Self-organising Urban Traffic control on micro-level using Reinforcement Learning and Agent-based Modelling
论文作者
论文摘要
大多数交通流量控制算法地址交换周期的交通信号和灯光的适应。这项工作通过自组织的微观控制来解决交通流优化，将强化学习和基于规则选择的代理结合了行动选择，在城市环境中执行远程导航。即，代理代表的车辆会根据当地环境传感器改编其对重新穿线的决策。基于代理的建模和模拟用于研究对城市交通流的出现影响。统一的代理编程模型可以通过将人群传感任务作为附加传感器数据库进行模拟和分布式数据处理。基于代理的人造城市区域的基于代理的模拟的结果表明，仅通过学习的个人决策和基于本地环境传感器的个人决策和重新穿线而在路径长度和行进时间方面的部署就可以提高移动效率。
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.
