# Utilizing Different Machine Learning Techniques to Examine Speeding Violations
## 1 Introduction  
Traffic crashes are considered a significant cause of death in the United States (U.S.) [1]. These crashes occur for different reasons, one of them being speeding violations. Speeding can be defined as driving a vehicle at a speed greater than permissible according to the speed limit or prevailing road conditions. Speeding affects the drivers, pedestrians, environment, and vehicles. According to the National Highway Traffic Safety Administration (NHTSA), crashes that result from speeding cost more than $40 billion every year [2]. In 2019, 9478 people were killed by speeding in the U.S. In addition to that, driving a vehicle at high-speed increases air pollution because of increased fuel consumption [3]. There are several negative consequences of speeding. Increasing the speed raises the probability of an accident and its severity. Driving a vehicle at high speed means more time for danger identification and response, or more distance traveled before responding to the threat and stopping the vehicle, which increases the probability of an accident [4, 5]. In addition, increasing the speed raises the probability of death because of the high crash energy as the speed increases (the relation between the crash energy and the vehicle's speed is exponential) [6]. Pedestrian death risk redoubled 4.5 times for speed change from 50 to 65 km/h. On the other hand, occupant fatality risk at 65 km/h is 85% for the side-impact accident [7]. In Canada, wrong drivers' behavior leads directly to 65% of crashes and indirectly to 90% of them [8]. The likelihood of an accident and its severity increase with increasing vehicle speed. A 50 km/h or less speed limit should be set in urban areas [9]. In Jordan, it was found that the difference between design speed and speed limit wouldincrease the speed variance and its severity impact when the difference was more than 10 km/h [10; 11]. Speeding can be caused by several unusual factors, such as traffic jams, drivers' familiarity with roads, and the drivers having a luxury car, which, in turn, encourages speeding [12]. Several studies worldwide examined speeding violations. In Edmonton, Canada, [PERSON] et al. (2016) [13] looked at drivers' compliance with speed limits, considering weather conditions, the shape of the road, and the time of day. Data were analyzed using logistic regression. It was found that traffic volume, the posted speed limit, and the number of lanes positively affected compliance (i.e., a higher speed limit and increasing the number of lanes increased speed compliance). [PERSON] et al. (1995) [14] investigated the attitudes of Greek drivers toward speed limit violations. Many reasons were assessed to find the most influential ones. The most critical factors were underestimating the risk of speeding, being in a hurry, and overestimating how good you are at driving. In Queensland, Australia, Afghanistan et al. (2018) [15] studied drivers' speeding behaviors based on traffic characteristics and roadway geometric factors. A panel mixed logit fractional split model was developed for Queensland's speeding data. The results revealed that the number of speeding violations increased as the radius of the horizontal curve increases. On the other hand, speeding violations decreased with increasing speed limits. [PERSON] et al. (2018) [16] studied the effects of different factors on speeding violations by applying a generalized linear model in a comparative study between Shanghai, China and New York City, United States. The speeding rate was utilized to study the violations. Situational and characteristic factors were examined to find their effects on the speeding rate. The results indicated that 16 working hours or less and a driving distance of more than 400 km were the factors that affect speeding violations. In addition to that, it was revealed that speeding behavior had a higher probability in urban areas and in the morning hours (5:00-7:00 AM).