General Information

Title: Reproducible and Low-cost Sim-to-Real Environment for Traffic Signal Control
Authors: Yiran Zhang, Khoa Vo, Longchao Da, Tiejin Chen, Xiaoou Liu, Hua Wei
Institution: Arizona State University, USA
Citation: Zhang, Y., Vo, K., Da, L., Chen, T., Liu, X. and Wei, H., 2025, May. Reproducible and Low-cost Sim-to-Real Environment for Traffic Signal Control. In Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2025) (pp. 1-2).

Reproducible Sim-to-Real Traffic Signal Control Environment

As urban environments become increasingly populated and automobile traffic soars, with US citizens spending on average 54 hours a year stuck on the roads, active traffic control management promises to mitigate traffic jams while maintaining (or improving) safety.

LibSignal++ is a Duckietown-based testbed for reproducible and low-cost sim-to-real evaluation of traffic signal control (TSC) algorithms. Using Duckietown enables consistent, small-scale deployment of both rule-based and learning-based TSC models.

LibSignal++ integrates visual control through camera-based sensing and object detection via the YOLO-v5 model. It features modular components, including Duckiebots, signal controllers, and an indoor positioning system for accurate vehicle trajectory tracking. The testbed supports dynamic scenario replication by enabling both manual and automated manipulation of sensor inputs and road layouts.

Key aspects of the research include:

Sim-to-real pipeline for Reinforcement Learning (RL)-based traffic signal control training and deployment
Multi-simulator training support with SUMO, CityFlow, and CARLA
Reproducibility through standardized and controllable physical components
Integration of real-world sensors and visual control systems
Comparative evaluation using rule-based policies on 3-way and 4-way intersections

The work concludes with plans to extend to Machine Learning (ML)-based TSC models and further sim-to-real adaptation.

Highlights - Reproducible Sim-to-Real Traffic Signal Control Environment

Here is a visual tour of the sim-to-real work of the authors. For all the details, check out the full paper.

Duckietown sim-to-real testbed camera views of traffic intersections — Real-Time Camera Views of Intersections in LibSignal++

Sim-to-real Duckietown-based traffic control testbed with Duckiebot and signal setup — Physical demonstration of LibSignal++ testbed

Sim-to-real RL training pipeline based on Duckietown testbed for traffic signal control — Pipeline to train RL agents in simulators, sim-to-real transfer, and real testbed deployment and test

Abstract

Here is the abstract of the work, directly in the words of the authors:

This paper presents a unique sim-to-real assessment environment for traffic signal control (TSC), LibSignal++, featuring a 14-ft by 14-ft scaled-down physical replica of a real-world urban roadway equipped with realistic traffic sensors such as cameras, and actual traffic signal controllers. Besides, it is supported by a precise indoor positioning system to track the actual trajectories of vehicles. To generate various plausible physical conditions that are difficult to replicate with computer simulations, this system supports automatic sensor manipulation to mimic observation changes and also supports manual adjustment of physical traffic network settings to reflect the influence of dynamic changes on vehicle behaviors. This system will enable the assessment of traffic policies that are otherwise extremely difficult to simulate or infeasible for full-scale physical tests, providing a reproducible and low-cost environment for sim-to-real transfer research on traffic signal control problems.

Results

Three traffic control policies were tested over a number of experiment repetitions, evaluating each time traffic throughput, average vehicle waiting times, and vehicle battery consumption. Standard deviations for all policies were found to be within acceptable ranges, leading the authors to confirm the ability of the testbed to deliver reproducible results within controlled environments.

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Project Authors

Yiran Zhang is associated with the Arizona State University, USA.

Khoa Vo is associated with the Arizona State University, USA.

Longchao Da is pursuing his Ph.D. at the Arizona State University, USA.

Tiejin Chen is pursuing his Ph.D. at the Arizona State University, USA.

Xiaoou Liu is pursuing her Ph.D. at the Arizona State University, USA.

Hua Wei is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University, USA.

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