A Hybrid Bio-inspired Type-2 Fuzzy Reinforcement Learning Framework for Regional Traffic Signal Coordination Control
Yunrui Bi1,*, Qiliang Yang1, Qinglin Ding1, Bin Ran2, Kun Liu1, Mingjie Zhang1
1 School of Automation, Nanjing Institute of Technology, Nanjing, China
2 Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, USA
* Corresponding Author: Yunrui Bi. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084267
Received 19 April 2026; Accepted 13 July 2026; Published online 28 July 2026
Abstract
To improve regional traffic signal coordination under uncertain and dynamic traffic conditions, this paper proposes a hybrid Type-2 fuzzy reinforcement learning framework integrated with Beetle Antennae Search (BAS) and Deep Q-Network (DQN), named Type-2 fuzzy Beetle Antennae Search and Deep Q-Network (T2-BAS-DQN). In this framework, DQN remains active during online signal control, while the Type-2 fuzzy module provides uncertainty-aware correction for phase selection and green-time adjustment. BAS is used only in the offline training stage to optimize a low-dimensional parameter vector related to fuzzy correction, reward adjustment, and coordination pressure. A 3
× 3 Simulation of Urban MObility (SUMO) road network is constructed to compare the proposed method with Fixed-Time, Max-Pressure, CoLight, DQN, and related baselines. The results show that T2-BAS-DQN achieves lower queue length, shorter waiting time, and higher average speed under different traffic demand levels. In addition, a real-geometry SUMO simulation calibrated with field traffic counts from Jiangning District, Nanjing, is conducted to further examine its applicability. Compared with DQN in this calibrated scenario, T2-BAS-DQN reduces average queue length by 47.4%, decreases average waiting time by 24.4%, and increases average travel speed by 11.0%. These results indicate that the proposed framework provides competitive simulation performance and improves several traffic operation indicators in the tested scenarios.
Keywords
Regional traffic signals; Type-2 fuzzy control; beetle antennae search algorithm; deep Q network; coordination control
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