Predicting Road Accident Severity Using
Machine Learning
Presented by: Code Commandos
• Ojaswi Sonawane (Team Leader)
• Arya Pandey
• Dipti Kadam
• Jagruti Sakore
• Gauri Suradkar
Introduction
• Problem Statement:
– Road accidents are a growing public safety concern with
varying degrees of severity (minor injuries to fatal crashes).
– Accurate predictions of crash severity can enhance road
safety and emergency response strategies.
• Objective:
– Build a machine learning model to predict crash severity.
– Analyze key risk factors (e.g., road design, weather, driver
behavior).
– Offer actionable road safety recommendations.
• Dataset Overview:
– Historical accident data with
features like speed, vehicle
type, road conditions, and
more.
– Crash severity levels: Minor
Problem Injury, Major Injury, Fatal
Crash.
• Challenges:
Analysis – Balancing the dataset to
avoid bias.
– Selecting meaningful features
for accurate predictions.
– Ensuring the model
generalizes well.
• Methodology:
– Data preprocessing
(handling categorical and
numerical data, missing
values).
– Feature engineering to
identify key contributors to
Proposed crash severity.
– Using a Gradient Boosting
Classifier for predictions.
Solution • Hyperparameter Optimization:
– Performed with
GridSearchCV for
parameters like
n_estimators,
learning_rate, and
max_depth.
Model Performance
• Accuracy:
• Achieved {accuracy}% accuracy on the test set.
• Confusion Matrix:
• Visualized using a heatmap.
• Highlights predictions for Minor, Major, and Fatal
crashes.
• Classification Report:
• Precision, Recall, and F1-scores for each severity level.
Key-Insights
Feature importance:
• Highlight the top 3-5 factors contributing to severe crashes
(e.g., speed, alcohol consumption, road conditions).
Risk patterns:
• Higher severity in wet/icy conditions.
• Influence of road type and vehicle type on crash outcomes.
Recommendations • Road Safety Improvements:
– Implement stricter speed
limits in high-risk zones.
– Enhance road surface
maintenance and signage.
– Increase awareness programs
for seatbelt usage and alcohol
consumption risks.
• Policy Interventions:
– Monitor and optimize urban
and rural road designs.
– Incorporate real-time
weather and traffic
monitoring systems.
• Short-Term:
– Better emergency response
through severity prediction.
• Long-Term:
– Reduction in accidents and
fatalities by identifying and Impact
mitigating risk factors.
– Data-driven policymaking for
road safety enhancements.
Conclusion
• Summary:
• Successfully built a model achieving the required accuracy.
• Identified key crash severity factors and proposed actionable
recommendations.
• Future Work:
• Integrate additional data sources (e.g., real-time traffic data).
• Explore advanced models like deep learning for improved
accuracy.
Thank you for your
attention!
