Introduction

In today’s data-driven world, traffic safety and crash data analysis are critical for informed decision-making, policy development, and risk mitigation. Leveraging the power of R programming, this course equips participants with advanced techniques to analyze, visualize, and interpret crash datasets effectively. Participants will learn how to harness data analytics, statistical modeling, predictive analytics, and machine learning in R to identify patterns, trends, and high-risk zones. Using R for Crash Data Analysis Training Course focuses on practical application, providing real-world case studies, data visualization dashboards, and reproducible workflows, enabling data professionals, transport engineers, and safety analysts to transform raw crash data into actionable insights.

The course emphasizes a hands-on approach, combining interactive coding exercises, real crash datasets, and scenario-based learning to develop analytical proficiency. Participants will gain expertise in data cleaning, data transformation, exploratory data analysis (EDA), regression modeling, and geospatial analysis using R. By integrating predictive modeling, network analysis, and visualization techniques, this training ensures participants can make data-driven decisions to enhance road safety strategies, support accident prevention programs, and optimize resource allocation. Whether you are a beginner or a professional seeking to enhance your analytical skillset, this course provides a structured path to mastering R for traffic crash analysis.

Programme Curriculum

Using R for Crash Data Analysis Training Course

Introduction

Course Duration

10 days

Course Objectives

Understand the fundamentals of R programming for crash data analysis.
Apply data cleaning and preprocessing techniques to traffic datasets.
Perform exploratory data analysis (EDA) for crash patterns.
Utilize statistical modeling to identify key crash factors.
Implement predictive analytics for accident risk forecasting.
Visualize crash data using ggplot2, plotly, and interactive dashboards.
Conduct geospatial analysis to identify accident hotspots.
Integrate machine learning models for crash severity prediction.
Develop reproducible workflows using R Markdown and Shiny apps.
Perform trend analysis and time series modeling for traffic safety.
Conduct network analysis to study road segment vulnerabilities.
Interpret results for data-driven decision making in road safety planning.
Analyze real-world crash datasets through case studies and scenario-based exercises.

Target Audience

Traffic safety analysts
Transport engineers
Road safety researchers
Data analysts and data scientists
Government transportation agencies
Urban planners and infrastructure professionals
Insurance analysts and risk managers
Academicians and students in traffic and transportation studies

Course Modules

Module 1: Introduction to R for Crash Data Analysis

R interface, IDEs, and packages overview
Understanding crash datasets
Loading and importing data
Introduction to data frames and structures
Case Study: National traffic crash dataset exploration

Module 2: Data Cleaning and Preprocessing

Handling missing values
Data type conversions
Removing duplicates and outliers
Standardizing categorical variables
Case Study: Cleaning multi-year crash datasets

Module 3: Exploratory Data Analysis (EDA)

Descriptive statistics for crash data
Frequency distribution of crash types
Visualizing trends over time
Correlation analysis of variables
Case Study: Identifying crash hotspots

Module 4: Data Visualization

ggplot2 basics and customizations
Interactive visualizations with plotly
Visualizing geospatial data
Dashboard creation using Shiny
Case Study: Crash severity dashboard

Module 5: Statistical Analysis

Hypothesis testing for crash factors
Regression analysis: linear and logistic
ANOVA and chi-square tests
Model validation and interpretation
Case Study: Factors influencing crash severity

Module 6: Predictive Analytics

Building predictive models in R
Feature selection and engineering
Model evaluation metrics
Scenario-based crash predictions
Case Study: Forecasting high-risk intersections

Module 7: Machine Learning Applications

Decision trees and random forests
Support vector machines (SVM)
Model tuning and cross-validation
Ensemble learning techniques
Case Study: Predicting crash types using ML

Module 8: Geospatial Analysis

Introduction to GIS in R
Mapping crash data using sf and leaflet
Spatial clustering and hotspot analysis
Kernel density estimation
Case Study: Urban traffic accident mapping

Module 9: Time Series Analysis

Trend and seasonality detection
Moving averages and smoothing techniques
ARIMA modeling for crash trends
Forecasting future accident rates
Case Study: Monthly crash trend prediction

Module 10: Network Analysis

Understanding traffic network graphs
Identifying critical nodes and segments
Graph theory metrics in road safety
Accident propagation analysis
Case Study: High-risk road segment analysis

Module 11: Crash Severity Modeling

Logistic regression for severity prediction
Multi-class classification
Risk factor identification
Scenario simulation for mitigation strategies
Case Study: Urban vs rural crash severity

Module 12: Reporting and Communication

Creating reproducible reports using R Markdown
Exporting tables, charts, and dashboards
Summarizing insights for stakeholders
Visual storytelling for traffic data
Case Study: Annual traffic safety report generation

Module 13: Shiny App Development

Introduction to Shiny framework
Interactive dashboard design
User input and reactive elements
Deploying web-based analytics tools
Case Study: Real-time crash monitoring dashboard

Module 14: Advanced Analytics Techniques

Clustering and segmentation of crash data
Principal component analysis (PCA)
Predictive maintenance analytics
Integration with external datasets
Case Study: Segmenting high-risk driver profiles

Module 15: Capstone Project

End-to-end crash data analysis project
Data preprocessing, modeling, and visualization
Presentation of actionable insights
Peer review and evaluation
Case Study: Comprehensive city-level crash data analysis

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

R / Python

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000