# Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
## 1 Introduction  
The Federal Highway Administration's (FHWA) Traffic Incident Management (TIM) program provides direct support to state agencies through the provision of novel tools and techniques designed to improve the detection, response to and resolution of traffic incidents as quickly as possible. Importantly, the goal of the TIM Program is to \"continuously improve the safety of responders and road users, the reliability of travel, and the efficiency of incident and emergency response\" [1]. Since 1996, all 50 states have passed \"Move Over\" laws designed to improve the safety of first responders and other roadside workers. Generally, these laws require motorists to change lanes in order to leave the lane adjacent to the responder or worker free from traffic. When changing lanes is not possible, these laws require motorists to slow down by a set amount from the posted speed limit. By freeing up the lane adjacent to the responder or worker, a \"buffer\" is created in an attempt to reduce the likelihood of a collision by passing motorists. When a motorist is unable to change lanes, the required speed reduction is also intended to minimize the chance of a collision. Although Move Over laws have been in place for many years, the number of responders and other roadside workers struck by passing motorists continues to be a significant safety problem. Nearly 30 roadside workers, first responders, tow truck drivers, or otherfield personnel supporting roadway operations are killed every year due to the failure of motorists to comply with Move Over laws. In fact, annual work zone deaths have risen from 586 in 2010 to 857 in 2020 [2], and FHWA has found that secondary accidents account for more than 20% of all crashes. In the past decade, various efforts have been employed to improve roadside worker and responder safety, including the use of new technologies, some of which have been developed by state transportation agencies in-house. Many of these solutions complement existing Move Over laws by, for example, deploying improved incident barriers and using real-time alerts regarding the need to \"Move Over\" due to the presence of workers or responders in the motorist's direction of travel. Despite these efforts, the number of injuries to workers and responders remains high. Beginning in September 2020, FHWA issued an award to Battelle Memorial Institute, the University of Maryland's Center for Advanced Transportation Technology (UMD CATT), and the Volpe National Transportation System Center to conduct a comprehensive, data-driven assessment of existing Move Over laws in the United States. This effort included large dataset analytics covering multiple states and regions as well as automated incident scene observations using state-of-the-art object detection and tracking (ODT) technologies already developed and deployed by UMD CATT. The ODT solution has been modified to focus on tracking vehicles as they travel by first responders (transportation, Fire/EMS, and/or law enforcement) who are stopped on the highway with alert lights engaged. In our study, we would like to investigate the problem of Move Over law compliance rates depending on various conditions, such as advance warnings or incident type, whether there is any factor that significantly improves the compliance rates, and whether factors such as advanced knowledge of the incident or its seriousness have no impact. Data derived from this research can be leveraged to improve motorist compliance with \"Move Over\" laws. Through the isolation of specific factors, including but not limited to, incident type, responder vehicles present (e.g., law enforcement versus transportation), roadway topography, and time of day, more specific public awareness campaigns and/or targeted notifications can be implemented to increase motorist compliance. In addition, further analysis of these data can help to inform changes to \"Move Over\" laws in order to better target enforcement around specific incidents types, locations, and times that may improve overall compliance.