Data Visualization of Traffic Violations in Maryland, U.S.

Abstract

Nowadays, car use has become so common and inevitable that with a high approximation, it can be said that every family has at least one car. This study analyzes traffic violation data from Montgomery County, Maryland to identify patterns and factors influencing road safety. A dataset with over 1 million records on traffic stops was explored using R and Python. Analysis focused on the most frequent stop causes, seasonal and hourly distribution of stops, and the role of alcohol. Results indicate that failure to obey traffic devices was the top stop reason. Stops peaked in summer months and nighttime hours. The age group with the highest accident rate was young males in their 20s. While alcohol impaired driving is a major concern, the data did not show a significant link between alcohol use and fatalities or injuries. This research provides useful insights into road safety patterns and risk factors. The methodology of data mining and visualizing a large traffic violations dataset demonstrates an effective approach for uncovering actionable insights. Key findings on high-risk driver demographics and stop causes can inform policies to improve road safety.

Keywords

• traffic violations
• classification models
• data visualization
• python
• data analysis research