Multimodal adaptive traffic signal control: A decentralized multiagent reinforcement learning approach

Kareem Othman , Xiaoyu Wang , Amer Shalaby , Baher Abdulhai
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Abstract

Public transit is considered a compelling alternative to the car, renowned for its affordability and sustainability, given that a single transit vehicle can accommodate a substantially higher number of passengers compared to regular passenger vehicles. In urban areas, a significant portion of the travel time spent by street-running transit vehicles is consumed waiting at traffic signals. Thus, transit signal priority (TSP) strategies have evolved over the years to give preference to transit vehicles at signalized intersections. Traffic signals are usually optimized for the general vehicular traffic flow, with TSP logic subsequently inserted as an add-on to modify the underlying signal timing plans, thereby granting priority to transit vehicles. However, one major issue associated with the implementation of TSP is its negative impact on the surrounding traffic, creating a conflict between prioritizing passenger vehicles versus transit vehicles. This paper proposes a novel decentralized multimodal multiagent reinforcement learning signal controller that simultaneously optimizes the total person delays for both traffic and transit. The controller, named embedding communicated Multi-Agent Reinforcement Learning for Integrated Network-Multi Modal (eMARLIN-MM), consists of two components: the encoder that is responsible for transforming the observations into latent space and the executor that serves as the Q-network making timing decisions. eMARLIN-MM establishes communication between the control agents by sharing information between neighboring intersections. eMARLIN-MM was tested in a simulation model of five intersections in North York, Ontario, Canada. The results show that eMARLIN-MM can substantially reduce the total person delays by 54 % to 66 % compared to pre-timed signals at different levels of bus occupancy, outperforming the independent Deep Q-Networks (DQN) agents. eMARLIN-MM also outperforms eMARLIN which does not incorporate buses and bus passengers in the signal timing optimization process.
多模式自适应交通信号控制:一种分散的多智能体强化学习方法
公共交通被认为是汽车的一个令人信服的替代品,以其可负担性和可持续性而闻名,因为与普通乘用车相比,一辆公共交通工具可以容纳更多的乘客。在城市地区,在街道上运行的交通车辆的很大一部分时间都花在了等待交通信号上。因此,多年来,交通信号优先(TSP)策略已经发展到优先考虑信号交叉口的交通车辆。交通信号通常针对一般车辆交通流进行优化,随后插入TSP逻辑作为附加组件来修改底层信号授时计划,从而赋予过境车辆优先权。然而,与TSP实施相关的一个主要问题是它对周围交通的负面影响,造成了优先考虑客运车辆与公交车辆之间的冲突。提出了一种新的分散多模态多智能体强化学习信号控制器,该控制器可以同时优化交通和公交的总延误。该控制器名为嵌入通信多智能体强化学习集成网络-多模态(eMARLIN-MM),由两个部分组成:负责将观测值转换为潜在空间的编码器和作为q网络进行时序决策的执行器。eMARLIN-MM通过在相邻的交叉口之间共享信息来建立控制代理之间的通信。eMARLIN-MM在加拿大安大略省北约克的五个十字路口的仿真模型中进行了测试。结果表明,eMARLIN-MM在不同的公交占用率水平下,与预定时信号相比,可以显著减少54%至66%的总人员延误,优于独立的深度q网络(DQN)代理。eMARLIN- mm在信号配时优化过程中也优于不考虑公交车和公交车乘客的eMARLIN。
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