This document summarizes an analysis of traffic violation data from 2017. It describes the dataset, data preprocessing steps including extraction, classification, oversampling, and imputation. Descriptive analysis examines relationships between variables like seat belt usage, gender, licenses and accidents. Modeling techniques applied include decision trees, logistic regression, neural networks, and auto neural networks. Models using only significant variables like description, time, violation type and day achieved similar accuracy. The analysis found accidents are more common for men than women and many drivers in accidents lacked proper licenses.

1. TrafficViolations
Business Analytics with SAS
Group No: 12
Neha Gokhale
Raviraj Khatu
Rugved Gramopadhye
Nikhil Bagewadi
Sarvesh Marathe

2. Introduction
&
Overview
•About the Dataset
•Data Preprocessing
•DescriptiveAnalysis
•ModelingTechniques
•Model Comparison
•Results and Implications

3. About
The
Dataset
• Traffic Violation Data 2017
Source: Data.gov
• Groups of Attributes
1. Location
2. Causes of Violation
3. Vehicle Information
4. Driver’s Information
5. Consequences of Violation
• Type of Attributes

4.  Huge Data
 Excess of categories in a column
 Imbalanced categories inTargetVariable
 MissingValues

5. Data
Preprocessing
 Data Extraction
 Data Classification
 Oversampling
 Impute Operation
 Data Partition

6. Data Extraction& DataClassification

7. Oversampling& ImputeOperation

8. Data PreprocessingSummary

9. Descriptive
Analysis
 Seat Belt as a cause of Accident
 Gender involved in anAccident
 Relation between Commercial License andAccident
 Day-wise distribution ofTrafficViolation cases
 Different types ofViolations
 Vehicle types involved inTrafficViolation

10. DescriptiveAnalysis
Seat Belts as a cause of Accident Gender involved in an Accident

11. DescriptiveAnalysis
Day-wise distribution ofTrafficViolations
Relation between Commercial License and Accident

12. DescriptiveAnalysis
DifferentTypes ofViolations Vehicle types involved inTrafficViolation

13. Modeling
Techniques
 DecisionTree
 Logistic Regression
 Neural Network
 Auto Neural Network

14. Data Modeling
using all input
variables
DecisionTree
 Simple and Easy to use
 Suitable for binary target variable
ImportantVariables
 Type of violation
 Personal Injury
 Property Damage
 Charge
 Description

15. Data Modeling
using all input
variables
Logistic Regression
 Recommended for binary target variables
 Uses Maximum Likelihood to estimate the model
parameters
ImportantVariables
 Day ofWeek
 Hour of Day
 Personal Injury
 Property Damage
 ViolationType
 Description

16. Data Modeling
using all input
variables
Neural Network
• It is supervised machine learning algorithm
• Data partitioned into
 Train – 70%
 Validation – 15%
 Test – 15%
Train Validation Test
0.0394 0.5042 0.4938

17. Data Modeling
using all input
variables
Auto Neural Network
• More Flexible than Neural Network
• We can specify number of Hidden
Units
Number of
Hidden Units
Misclassification
Rate - Train
Misclassification
Rate - Validate
Misclassification
Rate - Test
1 0.0 0.50 0.48
2 0.49 0.5 0.5021
3 0.46 0.57 0.56

18. Significant
Variables
 Description
 Hour of Day
 ViolationType
 Day ofWeek

19. Data Modeling
using only
significant
variables as
inputs
DecisionTree Logistic Regression
Predicted
Positive Negative
Actual
Positive 106 12
Negative 31 87
Predicted
Positive Negative
Actual
Positive 106 12
Negative 33 85
Accuracy = 81.77% Accuracy = 80.77%

20. Data Modeling
using only
significant
variables as
inputs
Neural Network Auto Neural Network
Predicted
Positive Negative
Actual
Positive 106 12
Negative 31 87
Predicted
Positive Negative
Actual
Positive 99 19
Negative 30 88
Accuracy = 79.23%Accuracy = 81.77%

21. Final Model

22. Model
Comparison

23. Results &
Implications
1. Accidents can happen on any day
2. Men are more involved in an accident and not women
3. The Majority drivers involved in accidents were not having the
official driving license
4. Can be useful for driving license department to show the people
the importance of proper training and seat belts

24. References
 https://support.sas.com/kb/24/205.html
 https://catalog.data.gov/dataset/traffic-violations-56dda
 https://support.sas.com/resources/papers/proceedings15/SAS196
5-2015.pdf
 http://support.sas.com/publishing/pubcat/chaps/57587.pdf
 https://support.sas.com/rnd/app/stat/papers/logistic.pdf

25. ThankYou !