# Resilient distributed model-free adaptive traffic signal control via controller dynamic linearization under DoS attacks

## TL;DR

This paper introduces a new traffic signal control system that adapts to cyberattacks and improves traffic flow during disruptions.

## Contribution

A novel resilient distributed model-free adaptive traffic signal control strategy (CDL-DMFAC) is proposed with DoS attack compensation.

## Key findings

- CDL-DMFAC reduces average queue length by 28.3% under high traffic and DoS attacks.
- The method achieves 36.32% reduction in average waiting time compared to conventional systems.
- Performance advantages increase with higher attack severity and traffic demand.

## Abstract

The increasing complexity and persistent network security challenges in traffic signal control are key issues requiring urgent attention to meet growing traffic demands. To address these issues, this paper proposes a resilient distributed model-free adaptive traffic signal control strategy (CDL-DMFAC) that integrates controller dynamic linearization (CDL) with multi-agent modeling. In the proposed framework, each signal phase at an intersection is modeled as an independent agent, and a compact form dynamic linearization (CFDL) is employed to construct an unknown ideal controller, enabling balanced control of multi-phase queue lengths. Furthermore, a denial-of-service (DoS) attack compensation mechanism is designed to mitigate the negative impact of communication interruptions or delays on signal timing decisions. Experimental results show that CDL-DMFAC effectively suppresses queue growth and delay accumulation under various attack intensities, with its performance advantage becoming more pronounced as attack severity increases. Notably, under the most challenging scenario—high traffic demand with multiple intersections simultaneously subjected to DoS attacks—the proposed method achieves reductions of 28.3% in average queue length and 36.32% in average waiting time compared to conventional signal control methods. These results highlight the method’s strong resilience against attacks, operational stability, and potential for deployment in larger-scale urban traffic networks.