This document summarizes a traffic safety analysis project that aimed to identify risk factors in fatal collisions in Toronto. The analysis focused on extensive traffic collision data to gain insights. Key findings included that certain streets, vulnerable road users like pedestrians, and factors like speeding and aggressive driving were prominent in fatal incidents. The analysis also examined temporal patterns and characteristics of involved individuals. Machine learning models were able to accurately predict speeding incidents based on variables like traffic control type and road class with over 77% accuracy. The analysis identified opportunities to refine models and collaborate with stakeholders to use insights for preventing collisions.

1. Toronto Traffic Safety Analysis:
Unveiling Risk Factors in Fatal Collisions
Professor: Richard Boire
CAPSTONE DATA PROJECT
Vishal Sang 101458132
Yiran Hu 101442783

2. Agenda
Introduction 3
Approach 6
Analytical Results 13
Key Findings 20
Conclusions 21

3. Background
Overview of the Company:
• TPS mission: "To Serve and Protect."
• Role in maintaining public safety and upholding
the law in Toronto.
• Partnership with the community for safety, law
enforcement, and emergency services.
• Commitment to transparency and continuous
improvement.
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4. Business Problem
4
• Data Overload
• Public Safety
• Community Engagement
• Resource Allocation
Key Expectations of the Client:
• Insightful Analysis
• Predictive Analytics
• Strategic Recommendations
• Community-Oriented Solutions
Key Challenges of the Client:

5. Identification of the Problem
5
Need to effectively analyze and
interpret extensive traffic collision
data for enhanced public safety.
Specific Challenges:
• Understanding Collision Patterns
• Addressing Road Safety Issues
• Data-Driven Decision Making
Primary Problem:

6. Approach
6
Data
Diagnostics
Selected
Variables
Conversion
to Numeric
Correlation Analysis

7. Data Diagnostics
7
Missing Values

8. Data Diagnostics
8
Unique Values

9. Data Diagnostics
9
Unique Values

10. Frequency Distribution Reports
• Significant occurrences on specific
streets like "LAWRENCE AVE E,"
"FINCH AVE W," and "EGLINTON AVE E"
highlight high-risk locations.
• Pedestrians (517 incidents) are notably
vulnerable road users in fatal collisions.
• Contributing factors include
speeding (187 incidents),
aggressive driving (427
incidents), and alcohol
involvement (44 incidents).
• Temporal patterns across 846
unique dates and 629 unique
times emphasize the diverse
circumstances of fatal
collisions.

11. Appendix

13. Derived Variables

14. Selected Variables
The Target Variable:'SPEEDING', because collisions involving
speeding are more likely to be severe.
Other Variable:
TIME, INVAGE, ROAD_CLASS, TRAFFCTL, RDSFCOND,
LIGHT, ALCOHOL
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15. Correlation
15
ALCOHOL
a positive correlation
INVAGE
a significant negative correlation
Other
a smaller negative correlation

16. The Exploratory Data Analysis
16
Traffic Control
Certain types of traffic control are more commonly
associated with collisions.
1.0 refers to No traffic Control and 5.0 refers to
Traffic Signal.
This shows that there were most no. of collisions
in the places where traffic wasn’t being controlled.

17. The Exploratory Data Analysis
17
INVAGE
• The age distribution shows a wide range,
indicating that individuals of various ages are
involved in collisions.
• There appears to be a higher frequency of
younger individuals involved in speeding and
collisions.

18. The Exploratory Data Analysis
18
Road Class
• The distribution across different road classes shows a varied number of
collisions associated with each class. This variation can be indicative of
the traffic volume, road conditions, or other factors specific to each
road class that may influence the occurrence of speeding-related
accidents.
• 1.0 refers to Expressway Ramp
• 6.0 refers to Major Arterial
• Major Arterial road class have a noticeably higher count of
collisions, suggesting that these classes might be more prone
to accidents or have higher traffic flow.

19. The Exploratory Data Analysis
19
LIGHT
The distribution across different light conditions
shows variability in collision occurrences.
1.0 refers to Dark light Condition
4.0 refers to Day Light Conditions
This Shows that most no. of Collisions
happen in Day light and Dark light
Conditions.

20. The Exploratory Data Analysis
20
Alcohol
• The majority of collisions did not involve alcohol,
as indicated by the higher count for '0' (No
Alcohol Involvement).
• However, there is still a notable number of
incidents where alcohol was involved,
highlighting its significance in traffic accidents.

21. ROC Curve
21
ROC Curve Overview:
• Shows how well the model distinguishes
instances with and without speeding.
AUC Score Significance:
• Quantifies overall model performance;
higher values indicate better discrimination.

22. Decile/Gains Chart
22
• Reveals how accurately the model ranks instances
by predicted probabilities.
• Divides the dataset into deciles, showing
cumulative true positive percentages for each.
• Visualizes alignment between model predictions
and actual instances of speeding.
• Steep increases indicate the model effectively
identifies a higher percentage of positive instances
within subsets.

23. Key Findings
23
Identified influential features: TRAFFCTL and ‘ROADCLASS' play crucial roles
in predicting speeding and Collision incidents.
Model accuracy: Achieved an overall accuracy of approximately 77.54% on
the test set.
ROC Curve and AUC Score: The ROC curve and AUC score (around 0.80)
demonstrate good discriminative power in distinguishing speeding incidents.
Decile/Gains Chart: Indicates the model's effective ranking of instances, with
a cumulative increase in true positive rate across deciles.

24. Conclusion
• Identified influential features: ‘TRAFFCTL’ and
‘ROADCLASS.'
• Suggested model refinement for improved accuracy.
• Emphasized ongoing validation, ethical deployment
considerations, and stakeholder collaboration for an
impactful solution in predicting and preventing speeding
and Collision incidents.
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25. Thank
you