VissimRL: A Multi-Agent Reinforcement Learning Framework for Traffic Signal Control Based on Vissim

TL;DR

VissimRL is a modular reinforcement learning framework that integrates high-fidelity Vissim traffic simulation with standardized Python APIs, enabling efficient development and testing of adaptive traffic signal control strategies.

Contribution

It introduces a standardized, modular RL framework for Vissim, facilitating multi-agent traffic signal control research with reduced development effort and improved simulation fidelity.

Findings

VissimRL reduces development complexity for RL in traffic control.

Supports multi-agent coordination and emergent behaviors.

Maintains high runtime efficiency during training.

Abstract

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.