# Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
## 2 Literature Review
Another study examined secondary crashes in Florida involving emergency incident responders in which breaking of Move Over laws occurred [9]. Crashes from 2011 to 2020 were investigated and 519 crashes with Move Over laws cited were found. The data showed that over half of accidents due to Move Over law violations happened on roads where the speed limit was 45 mph or lower. The study showed that in incidents where emergency responders were struck by vehicles, several factors were significant, including the operation on the side-facing traffic during the incident scene, law enforcement officers, and the absence of high-visibility safety appear. Although the majority of incidents happened on local roadways, towing operators were at higher risk of severe injuries on higher-speed roadways. [PERSON] et al. [3] examined driver behavior in some scenarios in Michigan, in which emergency/service vehicles were positioned on the freeway shoulder with their lights activated. Moreover, they investigated the effectiveness of using upstream Dynamic Message Signs (DMS) on driver behavior in these scenarios. The data of speed and lane positions were driven from emergency/service vehicles in two locations and vehicles on the closest lane to the right upstream of the DMS. They evaluated the compliance of driver with the Move Over law using logistic regression models considering factors such as vehicle type on the shoulder, and the displayed message on the DMS. The findings showed that the presence of a police vehicle on the shoulder has a significant impact on speed reduction compared to other responder vehicles such as transportation service vehicles. Additionally, it was discovered that the type of message shown on DMS did not have a notable influence on driver's behavior. There was only one exception, where motorists drive below the speed limit when seeing a move over message on the DMS compared to general travel time messages. Although previous studies have investigated Move Over law compliance and its impact on traffic safety, many of these studies have relied on traditional data analysis methods. In contrast, recent advancement in machine learning has shown that these models can be effective in the field of traffic safety [10; 11; 12; 13; 14; 15; 16]. Many studies have utilized machine learning algorithms for prediction of traffic accidents or high-risk areas, prediction of lane-changing behavior, and traffic monitoring. For instance, [PERSON] et al. [17] explored the integration of YOLOv5 and DeepSORT within a digital twin framework to enhance urban traffic management, demonstrating how deep learning can be leveraged for comprehensive vehicle detection and movement analysis. A similar approach has been also utilized for real-time vehicle detection and accident risk evaluation using the COCO dataset, which achieved a high detection accuracy [18]. The authors also evaluated the incident risk by computing detected object distances and potential accident points. Another study utilized Internet of Things (IoT) sensors with machine learning algorithms to detect potential hazards in roadway construction zones [19]. The high accuracy of the model indicated reliable results that could provide alerts to construction workers and minimize accident risk. Furthermore, deep learning approaches have been employed to predict lane change behavior and have offered some insights into vehicle trajectory patterns and improvements for decision-making in autonomous driving [20; 21]. Machine learning has also been applied in dynamic traffic management. One study utilized self-adaptive machine learning and real time traffic data, and it effectively detected and tracked vehicles moving in the wrong direction [22]. The results emphasized the important role that machine learning models have in managing traffic flow and addressing safety concerns in transportation systems.