Mapping Accident Trends and Patterns in Maryland

A Data-Driven Story

Data Visualization Course Projects | Aman-Anirudh-Ashutosh-Harsh-Soumya

Introduction

Road safety is a shared responsibility, but understanding what drives accidents can be complex. In Montgomery County, thousands of traffic accidents occur each year, influenced by a mix of environmental conditions, driver behavior, and vehicle design. This project brings these stories to life through a series of engaging visualizations, each highlighting a unique aspect of accident dynamics.

Starting with a geo-spatial map to pinpoint hotspots, the journey explores not just where accidents happen but also the factors behind them—like lighting, weather, and road conditions. One of the most novel visualizations, the Car-in-a-Clock, maps collision points onto a clock face to reveal how impact direction and vehicle type influence injury severity. From stacked bar charts to mosaic charts and a treemap, each visualization connects the dots between data and actionable insights. Along with these we also explored how car's country of origin and build year affect the severity of injuries. Together, they provide a fresh perspective on traffic safety, inviting everyone—from policymakers to everyday drivers—to join the effort to create safer roads.

Geospatial map for locations and accident severity

• Region Selection: Use the dropdown to filter by accident severity.

• Toggle Markers: Use the checkbox to show/hide accident markers.

• Legend: Displays severity color scale.

• Postal Region Details: Hover to view accident counts.

• Accident Details: Hover over location icons to see light conditions, collision type, vehicle details, and severity.

• Purpose: Identify high-risk areas and traffic accident patterns.

This map visualizes traffic accident data across Montgomery County, Maryland, highlighting accident severity, locations, and contributing factors. It displays postal areas with varying accident frequencies and severity, helping identify high-risk zones and accident hotspots. The map also shows the relationship between accident types, vehicle involvement, and road safety conditions. By analyzing this data, local authorities can implement targeted safety measures to improve road conditions and reduce accident rates.

Pie chart matrix for comparing accidents against car brands

• X-Axis: Displays the year range.
• Y-Axis: Shows car brands' countries of origin.
• Cells: Contain pie charts representing accident severity, with red borders for fatal injury cases.
• Hover Interaction: Enlarges the pie and displays exact severity percentages as text labels.

This pie chart matrix visualization maps accident severity based on the car brand's country of origin and the manufacturing year. The chart highlights how manufacturing defects or older car models may influence the frequency and severity of accidents.

Mosaic charts of Injury severity vs Light conditions

Select the two years you want to analyze from the dropdowns. The mosaic charts for the corresponding years will be shown. Hover over each tile for more information.

This mosaic chart illustrates the correlation between lighting conditions and injury severity in traffic accidents. Each segment represents a lighting condition, with color coding for various injury severities. Use this chart to identify and compare trends and patterns for two years that can inform road safety measures.

Stacked bar chart matrix for Weather conditions and Light Conditions plotted against No of Accidents

Hover over any box to see more interesting numbers and click for a tree map on road conditions.

The stacked bar chart compares traffic accidents by light and weather conditions, emphasizing driver fault. It highlights how daylight accidents often result from human error, while adverse weather (e.g., snow, rain) increases risks due to poor visibility and slippery roads. The visualization advocates for adaptive safety measures like public awareness and advanced vehicle technologies.

Car in a Clock: Spider chart comparing the number of accidents and injury severity

• Hover over blue points to see accident counts and orange points for injury severity details.

• Link data to real-world scenarios using the car-clock metaphor (e.g., '12 o'clock' = frontal impacts).

• Rotate the wheel to compare trends across vehicle types like SUVs or smaller cars.

• Click on the blue and orange button on the right to toggle the charts.

The Car-in-a-Clock visualization maps accident data onto a clock face, representing impact directions. At its center is a spider chart that compares accident frequency (blue) and injury severity (orange). Beneath the chart, a car silhouette links data points to real-world scenarios—each clock position corresponds to a specific collision direction (e.g., '12 o'clock' for frontal impacts). A wheel on the right lets users explore how accident trends vary by vehicle type, such as SUVs or smaller cars. This tool highlights patterns in accident dynamics and injury severity influenced by collision direction and vehicle design.