Urban traffic signal control robust optimization against Risk-averse and Worst-case cyberattacks

Abstract

Ensuring the cybersecurity of urban traffic signal control systems has become increasingly important in the digital age. This study proposes a passive countermeasure by designing robust signal control plans to mitigate the risks of worst-case and risk-averse cyberattacks. To achieve this objective, the study develops two bi-level simulation-based optimization (SO) models and solves them using an improved biobjective robust simulation-based optimization (IBORSO) algorithm. The attack-defense process is formulated as a Stackelberg game, where the lower level attacker aims to find optimal attack plans to degrade traffic efficiency and safety indices, while the upper level defender adjusts the signal control plan to compensate for the effects of the attack on traffic efficiency and safety indices. The proposed approach is evaluated on an urban road network in Changsha, China. The results show that the developed robust signal control plans can withstand risk-averse and worst-case attacks and perform better than the counterpart ones in terms of biobjective performance. The proposed bi-level modeling framework and solution algorithm can be used to build robust traffic signal control systems that are resilient to cyberattacks.