Abstract
Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.
Highlights
States, especially in the areas like West Texas with large rural road networks
Administration (NHTSA), in 2016, alcohol-impaired driving crashes accounted for 18% of drivers involved in fatal crashes in the age group from 16–20 [32]. This indicates that the current Texas alcohol regulation that bans anyone under the age of 21 to purchase or consume alcohol successfully reduced the occurrences of teen driver alcohol-related crashes in West Texas
About 4% of teen drivers were involved with crashes with serious injuries and 1% were involved with fatal crashes, which was much lower than the average level of teen fatal crashes in the nation
Summary
Especially in the areas like West Texas with large rural road networks. According to the Texas. The purpose of performing this EDA is to first to explore the collected raw dataset by using methods from descriptive statistics and data visualization This EDA is done without any pre-conceived notions or hypotheses, and the results of this exploration is used to guide and to identify the factors in the subsequent machine learning (ML) models. Step 2: By performing data dimensionality reduction in the first step, build four machine learning models to identify the most important factors associated with the severity of crashes on rural roads and predict crash severity using these identified factors.
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