## 2 Related Works  
RLR is an action that threatens the traffic system passing through by ignoring the signaling system at a signaled intersection. RLR is a serious problem that can lead to fatal traffic accidents as well as minor traffic violations. A collision between a violating vehicle and another vehicle legally passing through an intersection and a green traffic light is called an RLR collision. To avoid RLR-related collisions, it is important to identify factors that have a significant impact on the behavior of RLR drivers and to predict RLR likelihood in real-time [8]. [PERSON] et al. [9] proposed a connected vehicle based dynamic all-red extension (DARE) framework to prevent potential collisions due to RLR. The proposed method performs binary classification of RLR and Non-RLR based on non-weighted and weighted least square support vector machines (LS-SVM) using continuous trajectories measured by radar sensors. As a result, RLR and Non-RLR were classified with higher accuracy compared to other techniques based on conventional inductive loop detection. In [10], RLR prediction consists of two parts: arrival time and vehicle behaviors when the vehicle reaches the stop line. The proposed technique is a Bayesian network (BN) probability model based on continuous trajectories collected by radar sensors for RLR prediction. Based on the vehicle's speed, acceleration, and car-following behavior, and the causality of BN, RLR prediction performance was improved. In addition, the driving decision maker was provided with the predicted RLR probability and contributed to the improvement of traffic safety. [PERSON] et al. [11] proposed a camera-based RLR detection technique using a Single Shot Detector (SSD). In this study, researchers use cameras to collect data at intersections. The proposed system achieved RLR detection performance of 92.1% by applying a deep learning based approach. However, despite the high detection performance, the proposed technique focused on detection rather than the prediction of RLR as a camera-based technique. In [12], a random forest-based learning model was proposed to predict RLR violation. In addition, observation data and driver simulator data were used to analyze factors affecting RLR. According to the results of the proposed prediction model, the important factors for predicting RLRviolations are the distance between the vehicle and the intersection, time to intersection (TTI) and the speed of the vehicle at the yellow onset. In order to reduce accidents at intersections, techniques to control the traffic signal when RLR is detected has been proposed. In [13], a traffic signal countdown (CT) auxiliary device is used in order to reduce the RLR. The CT-based traffic light system aims to reduce RLR by providing the driver with the remaining time of green light. However, at the end of the green light's duration, the RLR may increase as the driver accelerates through the intersection before the signal changes. Likewise, if there is little red light remaining when reaching the intersection, the driver will not decelerate and may enter the intersection early and an RLR may occur. Control of the yellow signal interval had a positive effect on the reduction of RLR [14]. Control of the yellow signal interval helped the driver to make a driving decision at the intersection. According to the study, the time duration of the yellow signal that most effectively reduces RLR is 5 s, and when the duration of the yellow signal exceeds 5 s, RLR is increased again. Since this method is a fixed yellow signal setting, the effect is reduced if the driver gets used to the yellow signal in the long term. [PERSON] et al. [15] proposed an extension of the yellow signal and an enforcement system using a Red Light Camera (RLC). The incidence of RLR was reduced by 36% by increasing the duration of the yellow stop light by 1 s. In addition, by applying an enforcement system using RLC, the RLR incidence rate was reduced by more than 96%. [PERSON] et al.