# Utilizing Different Machine Learning Techniques to Examine Speeding Violations
## 1 Introduction
In Asia, [PERSON] (2013) [17] conducted a study in Taiwan to examine the relationship between speeding violations and the social-economic characteristics of the drivers. A dataset of 8129 drivers was analyzed using logistic regression analysis. It was concluded that the age group (20-29) tends to violate speed. An increasing income level was one of the factors associated with increasing speeding violations. Furthermore, driving for various purposes impacted increasing speeding violations, especially driving for business and leisure. In China, [PERSON] et al. (2014) [18] studied speeding violations between 2006 and 2010 by examining different risk factors. The independent factors were analyzed using stepwise logistic regression to specify the influencing factors. The analysis illustrated that a high risk of speeding violations was associated with male drivers, the dark, morning time, and accident years. On the other hand, public holidays, days of the week, weather conditions, age, and season did not affect speeding violations. Another study in China conducted by [PERSON] and [PERSON] (2020) [19] studied speeding behavior using Planning Behavior Theory (PBT). An ANOVA analysis was employed to examine the effect of the demographic and descriptive characteristics on speeding behavior. It was revealed that male drivers, the age group (22-44), and high-income level were the factors that increased the violations. In Kuwait, [PERSON] et al. (2020) [20] studied the factors that impact drivers' speeding behavior. A \(t\)-test and an ANOVA analysis were used to analyze data from 536 drivers. Speed Related Score (SRS) values were used to specify the influencing factors. The outcomes showed that male, single, Kuwaiti drivers, and the age group (18-24) were most likely to violate speed limits. In India, [PERSON] and [PERSON] (2021) [21] studied speeding violations using logistic regression. The results revealed that male drivers and young ones (less than 25 years old) had a greater speeding violation rate. The environment and road variables revealed that fine weather, daylight, central divider, darkness with no street lighting, light motor vehicles, single-lane roads, and uncontrolled junctions affected speeding behavior. On the other hand, the day of the week and season did not affect speeding violations. In Jordan, [PERSON] et al. (2022) [22] investigated how speeding violations may affect the driver's environment, the vehicle, the road, and the weather. Results showed that speeding violations depended on age, type of vehicle, speed limit,day of the week, season, year of the accident, time of the accident, license category, and condition of lights. In many areas of engineering branches, learning algorithms, neural networks, and modern adaptive fuzzy systems are used a lot for various tasks and applications [23; 24]. Analyzing traffic-related datasets has recently gained interest in machine learning techniques [22; 23; 24; 25; 26; 27; 28]. Researchers are interested in these methods because they can deal with vast amounts of data and find correlations between variables that would be hard to find using standard statistical modeling techniques. Several research studies investigated the effect of different factors on speeding violations, such as age [17; 18; 19; 20; 22], gender [18; 19; 20; 21], weather conditions [13; 18; 21; 22], light conditions [13; 16; 18; 21; 22], and area type [16; 21; 22]. None of the earlier research looked at how the direction of the traffic flow, the condition of the road surface, the grade of the road, or the presence of work zones affected speeding violations. In addition to that, most of these studies used traditional methods of data analysis, such as logistic regression, panel mixed logit, generalized linear model, and analysis of variance (ANOVA). This research aims to study the effects of several variables on speeding violations in the United States using a variety of machine learning techniques, such as Random Forest (RF), Classification and Regression Tree (CART), and Multi-Layer Perceptron (MLP).