Abstract

Road accidents remain a major public safety concern in the United Kingdom. This project analyzes accident data to identify high-risk zones, major causes, seasonal patterns, and demographic impact. Using data analytics and visualization techniques, the system provides actionable insights to improve road safety policies, traffic management, and public awareness campaigns.

Existing System

1. Manual accident reporting and basic statistical analysis.
2. Limited real-time monitoring.
3. Static reports without interactive dashboards.
4. Difficulty in identifying accident hotspots dynamically.
5. Less predictive analysis capability.

Proposed System

1. Centralized accident data collection and preprocessing.
2. Interactive dashboards using BI tools.
3. Accident hotspot identification using geospatial analysis.
4. Predictive modeling to forecast accident-prone areas.
5. Cause-based trend analysis (speeding, weather, alcohol, etc.).
6. Data-driven safety recommendations.

Software Requirements

1. Python (Pandas, NumPy, Matplotlib, Seaborn)
2. Power BI / Tableau
3. Jupyter Notebook
4. SQL (MySQL/PostgreSQL)
5. Excel
6. Operating System: Windows / Linux / macOS

Hardware Requirements

1. Minimum 8GB RAM (16GB recommended)
2. Intel i5 or higher processor
3. 256GB SSD storage
4. Stable internet connection
5. GPU (optional for advanced ML models)

Advantages

1. Identifies accident hotspots accurately.
2. Supports government safety planning.
3. Helps reduce accident rates through predictive insights.
4. Easy visualization for decision-makers.
5. Improves public awareness.

Disadvantages

1. Requires high-quality, clean datasets.
2. Data privacy and security concerns.
3. Predictive models may have accuracy limitations.
4. Maintenance and regular updates required.