ANALYSIS OF ROAD TRAFFIC FATAL
ACCIDENTS USING DATA MINING
TECHNIQUES
MEET THE TEAM
G PARAMESHWAR
AYILA PRIYA FAHMEEDA SHAIK K MAHESH
TEAM LEADER TEAM MEMBER TEAM MEMBER TEAM MEMBER
HTNO:21D01A6711 HTNO:21D01A6733 HTNO:21D01A6754 HTNO:21D01A6739
CONTEXT
1. Abstract
2. Introduction
3. Problem statement and objectives
4. Data mining techniques for accident analysis
5. Existing system
6. Proposed system
7. System requirements
8. System architecture
9. Future work
10. Conclusion
ABSTRACT
Road accidents are the main cause of death as well as serious injuries in the world. In order to stay
safe, careful analysis of roadway traffic accident data is important to find out factors that are
related to fatal, serious injury, minor injuries, and non-injuries. The relationship between fatal rate
and other attributes include combining weather conditions, road type, sunlight conditions, speed
limit, drunk driver and so on are considered. Here, three data mining algorithms namely Apriori,
Naïve-Bayes and K Means are applied on the accident dataset to predict the accident severity.
Apriori Algorithm is used for finding association rules between the attributes. Naïve-Bayes based
approach is used for classifying how attributes are conditionally independent. K means is used to
form clusters and analyze them based on attributes. Comparison based on parameters is done to
prove the efficiency of the various road accident detection techniques and approaches. By using
this analysis, government/private agencies can take decisions in developing new roads and taking
additional safety measures for the general public and awakening a sense of responsibility among
road users.
INTRODUCTION
Road traffic accidents are a major public health concern, resulting in over
1.3 million fatalities worldwide each year.
Factors like driver behavior, road conditions, vehicle design, and
environmental influences all play a role in fatal accidents
Analyzing accident data can uncover trends and patterns to guide effective
prevention strategies.
This study provides road accident detection techniques by analyzing the
novel ideas
The comparison of the techniques used here, that is Apriori, Naive-Bayes
and K-Means is carried out in terms of precision and recall.
PROBLEM STATEMENT AND
OBJECTIVES
Problem Statement
To use data mining techniques to identify the key factors contributing to
road traffic fatal accidents and inform prevention efforts.
Objectives
Analyze accident data to uncover patterns and trends
Identify the most significant risk factors for fatal accidents
Develop predictive models to anticipate and mitigate accidents
Provide data-driven recommendations for improving road safety
DATA MINING TECHNIQUES FOR
ACCIDENT ANALYSIS
Data Collection
Gather comprehensive datasets from various sources, including police reports, traffic cameras, and
transportation authorities.
Data Preprocessing
Clean, transform, and integrate the data to ensure it is ready for analysis.
Modeling and Analysis
Apply advanced data mining techniques like regression, clustering, and decision trees to uncover
patterns and insights.
EXISTING SYSTEM
Manual Reporting
Current systems rely on manual processes for collecting and analyzing accident data, limiting the scope and
timeliness of insights.
Reactive Approach
Existing strategies focus on responding to accidents, rather than proactively preventing them through data-driven
insights.
Fragmented Data
Accident data is often scattered across multiple sources, making it challenging to gain a comprehensive
understanding.
Limited Automation
The lack of automated data processing and analysis tools hinders the timely identification of emerging accident
patterns.
PROPOSED SYSTEM
Automated Data Collection: Utilizing IoT devices and real-time data feeds for continuous
data collection
Automated Data Processing: Implementing automated scripts and tools for data
cleaning, integration, and transformation
Advanced Data Analysis: Applying more sophisticated algorithms like FP-Growth for
association rules, advanced classification models, and enhanced clustering techniques
Predictive Modeling: Developing predictive models to forecast accident hotspots and
potential causes
SYSTEM REQUREMENTS
SOFTWARE HARDWARE
1. Operating System: 64-bit Windows 10 or later, 1. Processor: Multi-core processor (at least 4
or Linux distributions cores) with a minimum speed of 2.5 GHz
2. Data Mining Software: R, Python, SPSS, SAS, 2. RAM: 16 GB or more (32 GB or more
or Oracle Data Mining recommended)
3. Data Visualization Software: Tableau, Power 3. Storage: 1 TB or more hard disk drive (HDD)
BI, QlikView, or D3.js or solid-state drive (SSD) (2 TB or more
4. Database Management System: Relational recommended)
databases (e.g., MySQL) 4. Graphics Card: High-performance graphics
5. Geospatial Analysis Software: ArcGIS, QGIS, card with at least 4 GB VRAM
or Google Maps 5. Display: 22-inch or larger monitor with
resolution of 1920x1080 or higher
SYSTEM ARCHITECTURE
FUTURE WORK
Predictive Modeling
Develop more accurate predictive models to anticipate and prevent fatal accidents.
2. Autonomous Vehicle Integration
Explore how self-driving car technologies can be leveraged to improve road safety.
3. Real-Time Data Integration
Incorporate real-time traffic and environmental data to enable dynamic risk assessment.
CONCLUSION
The aim of this study was to show the application of data mining techniques
in the field of accident investigation.
The modeling will be to combine road related factors with driver information
for better prediction, and to find interactions between the different attributes.
The analysis of these methods provides a better understanding of the steps
involved in each technique to achieve accurate results.
It will be useful to the authorities as well as to the entire society for awareness.
So the implementation will be done to analyse the road accidents
THANK
YOU
