Traffic congestion remains a major challenge for urban transportation, leading to significant economic and environmental impacts. Traffic Signal Control (TSC) is one of the key measures to mitigate congestion, and recent studies have increasingly applied Reinforcement Learning (RL) for its adaptive capabilities. With respect to SUMO and CityFlow, the simulator Vissim offers high-fidelity driver behavior modeling and wide industrial adoption but remains underutilized in RL research due to its complex interface and lack of standardized frameworks. To address this gap, this paper proposes VissimRL, a modular RL framework for TSC that encapsulates Vissim's COM interface through a high-level Python API, offering standardized environments for both single- and multi-agent training. Experiments show that VissimRL significantly reduces development effort while maintaining runtime efficiency, and supports consistent improvements in traffic performance during training, as well as emergent coordination in multi-agent control. Overall, VissimRL demonstrates the feasibility of applying RL in high-fidelity simulations and serves as a bridge between academic research and practical applications in intelligent traffic signal control.

翻译：交通拥堵仍然是城市交通面临的主要挑战，导致显著的经济与环境影响。交通信号控制是缓解拥堵的关键措施之一，近年来研究越来越多地应用强化学习以利用其自适应能力。相较于SUMO和CityFlow，仿真器Vissim提供了高保真的驾驶员行为建模和广泛的工业应用基础，但由于其接口复杂且缺乏标准化框架，在强化学习研究中仍未得到充分利用。为填补这一空白，本文提出VissimRL——一个用于交通信号控制的模块化强化学习框架，通过高级Python API封装Vissim的COM接口，为单智能体与多智能体训练提供标准化环境。实验表明，VissimRL在保持运行效率的同时显著降低了开发工作量，支持训练过程中交通性能的持续提升以及多智能体控制中涌现的协同行为。总体而言，VissimRL证明了在高保真仿真中应用强化学习的可行性，并成为智能交通信号控制领域学术研究与实际应用之间的桥梁。