## 1 Introduction  
Traffic incidents are considered a high-priority problem for traffic safety. According to a recent report published by the World Health Organization (WHO), traffic incidents cause over 1.35 million fatalities and 50 million injuries annually [(1)]. If not cleared on time, traffic incidents have the potential to cause negative consequences, including increased non-recurring congestion, travel delays, secondary crashes, a reduction in roadway capacity, and risks to response personnel [(2)]. According to National Traffic Incident Management Coalition (NTIMC) estimates, traffic incidents are responsible for approximately 25% of all congestion on U.S. roadways, leading to an annual loss of about 2.8 billion gallons of gasoline [(3)]. Additionally, secondary crashes account for 20% of all the incidents in the U.S. [(4)]. Furthermore, in the year 2014, each individual in the United States was estimated to have experienced a total of 42 h of delay during peak hours, resulting in a collective loss of around USD 160 billion due to congestion [(5)]. Incident clearance time (ICT) can be defined as the time from when the incident is detected until clearance of all travel lanes [(6)]. Incidents that are not cleared on time may lead to an increase in incident duration by twice or thrice [(7)]. To mitigate these negative consequences resulting from traffic incidents, it is important to improve traffic incident management (TIM) efficiency. One TIM strategy involves providing travelers with accurate ICT estimates, allowing them to make an informed decision [(8; 9)]. Another strategy would be managing traffic flow by actively rerouting around the incident scene to avoid congestion. Both cases required accurate prediction and understanding of the factors affecting ICT. The _Highway Capacity Manual_ categorizes incident duration into four phases: detection time (the time between the occurrence of the incident to the time it is reported), response time (the time from when the incident is reported to the arrival of the first responder at the scene), clearance time (the time from the first responder's arrival to the time when the incident is cleared), and recovery time (the time taken for the traffic flow to normalize after the incident is cleared) [10]. ICT is of great importance in reducing the impact of traffic incidents among the different phases of incident duration, as it directly affects travel delays and congestion. ICT is one of the key performance measures of Louisiana's TIM. Rural interstates face the challenge of longer incident durations because of their distance from urban centers, accessibility issues, and the limited availability of emergency responders [11]. Due to their remoteness, ICT can be significantly increased, leading to increased travel delays, congestion, and secondary crashes. To overcome these challenges, it is important to reduce the duration of these incidents. Achieving this goal will require the accurate prediction and dissemination of ICT to both motorists, particularly those approaching the incident scene, and various agencies involved in incident management, such as the traffic management center (TMC), fire, police, and emergency medical services (EMS). This would allow stakeholders to make informed decisions regarding their travel. This study aims to develop a machine learning (ML)-based ICT prediction model for crash-related incidents in Louisiana's rural interstates and uses SHAP analysis to investigate influencing factors for these incidents. The two separate approaches were utilized in a complementary framework by taking advantage of the strengths of machine learning models and SHAP analysis. While machine learning models provide better predictions, their methodologies are considered black boxes and do not excel at establishing relationships between response and independent variables. SHAP analysis is flexible and useful for analyzing the impact and direction of the influence of variables on a phenomenon.