## 2 Related Works
[16] proposed a method to reduce RLR violations by dynamically allocating signal periods through a Wireless Sensor Network (WSN). The main goal is to dynamically change the green time based on the queue length, allocating a larger green time to the road with the longest queue. Experiments conducted in Philadelphia reduced RLR violations through dynamic assignment of traffic signal periods. However, changing signal settings under the influence of RLR can lead to an asymmetric traffic assignment problem. In [17], authors argued that two distinct problems can be formulated to address the asymmetric traffic assignment problem: First, the global optimization of signal setting and traffic assignment (GOSSTA) combined problem and second, the local optimization of signal setting and traffic assignment (LOSSTA) combined problem. Related to these problems, [PERSON] et al. [18] transformed the GOSSTA problem into a surrogate continuous optimization problem via a generalized surrogate problem methodology based on an online control scheme and solved the latter using a standard gradient-based approach. On the other hand, [PERSON] et al. [19] proposed an Ant Colony Optimization (ACO) algorithm to solve LOSSTA. The results of the proposed ACO algorithm for real networks were able to get the solution in a shorter time with the same accuracy as the conventional method of the successive averages (MSA) approach [20]. [PERSON] et al. [21] identified driver and vehicle characteristics that affect accidents using classification and regression tree techniques based on the 2012-2016 Isfahan crash database. In this study, the tree model divided drivers into three age groups: under 22.5 years old, 22.5 to 51.5 years old, and over 51.5 years old. It also suggested improving driver education, increasing traffic fines, and banning drivers with poor driving history to reduce RLR. [PERSON] et al. [22] proposed a step-by-step penalty strategy to prevent the re-offending of RLR vehicles. Despite the rigorous penalty strategy to reduce RLR, its effectiveness was limited. The reason is that traffic delays for other vehicles due to the potential risk of collision with RLR vehicles are not included. In addition, both unintentional and intentional RLRs are subject to the same penalties because the proposed system cannot make a clear distinction between unintentional RLR and intentional RLR. This may be unfair for unintended RLR violators. Conventional studies have focused on the binary classification of RLR and Non-RLR, and the extension of fixed time signals. Penalties were also effective in reducing RLR. Additionally, some studies have discussed penalty policies to reduce RLR. However, excessive penalties for unintended RLR are a problem to be solved. Our proposed system performs a specific classification of RLR based on features rather than binary classification of RLR and Non-RLR. This can contribute to the classification of unintended RLRs based on the characteristics of RLRs, and is expected to positively help in constructing a stronger RLR fines system. In addition, our proposed system can contribute to the improvement of safety and efficiency of the intersection traffic system based on the dynamic all-red signal extension conforming to the specified RLR class. As discussed above, while it is still possible that the proposed dynamic all-red signal extension may cause an asymmetric traffic assignment problem, it is not the primary focus of this paper to improve the overall traffic efficiency by solving the asymmetric traffic assignment problem as done in many aforementioned related works. Instead, we focus more on improving the safety of intersection traffic by preventing accidents due to RLRs and also the efficiency of it by overcoming the problem of conventional fixed signal extension mechanisms.