https://github.com/sarveshdhond/toronto_collision_analysis
Analyzing traffic collision data in and around Toronto, this project aims to uncover patterns and risk factors. The results offer crucial insights for understanding risk exposure, directly aiding in the establishment of appropriate insurance rates
https://github.com/sarveshdhond/toronto_collision_analysis
pandas python sql
Last synced: 3 months ago
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Analyzing traffic collision data in and around Toronto, this project aims to uncover patterns and risk factors. The results offer crucial insights for understanding risk exposure, directly aiding in the establishment of appropriate insurance rates
- Host: GitHub
- URL: https://github.com/sarveshdhond/toronto_collision_analysis
- Owner: SarveshDhond
- Created: 2025-06-21T21:25:58.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-21T21:31:23.000Z (about 1 year ago)
- Last Synced: 2025-06-21T22:25:55.543Z (about 1 year ago)
- Topics: pandas, python, sql
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# TORONTO TRAFFIC COLLISIONS ANALYSIS
## OVERVIEW
This repository analyzes traffic collision data from [Toronto Police Open Data](https://data.torontopolice.on.ca/datasets/TorontoPS::traffic-collisions-open-data-asr-t-tbl-001/explore) to identify patterns, risk factors, and high-severity zones. Our analysis focuses on temporal patterns, transportation mode vulnerabilities, geographic risk zones, and key safety metrics to inform urban planning and public safety initiatives.
## KEY INSIGHTS
### 1. Temporal Patterns
- **Peak Danger Hour**: 18:00 (6 PM) consistently shows highest accident rates across all months
- **Monthly Distribution**: Highest incidents during commuter-heavy months (March, April, October)
- **Weekly Analysis**:
| Day | Accidents | Fatalities |
|-----------|-----------|------------|
| Friday | 122,976 | 99 |
| Thursday | 117,467 | 104 |
| Wednesday | 113,817 | 95 |
| **Weekday Total** | 567,480 | 470 |
| **Weekend Total** | 171,781 | 157 |
### 2. Transportation Mode Risk Analysis
| Mode | Cases | Fatalities | Highest-Risk Incident |
|-------------|-------|------------|------------------------|
| Pedestrian | 1 | 3+ | March 2022 (Thursday) |
| Automobile | 1 | 3-4 | Oct 2024 (Thursday) |
| Motorcycle | 60 | 1 | Distributed 2014-2024 |
| Bicycle | 62 | 1 | Distributed 2014-2024 |
*Motorcycles/bicycles show lower fatality rates potentially due to seasonal use, safety gear, and reduced winter operation*
### 3. Pedestrian High-Risk Zones
| Location | Avg Severity | Risk Factors |
|---------------------------|--------------|--------------|
| Wexford/Maryvale | 0.66 | Arterial roads, limited crossings, high-speed turns |
| Harbourfront Cityplace | 0.63 | Tourist density, event traffic, mixed-use corridors |
| Morningside Heights | 0.62 | Steep terrain, academic institutions, poor lighting |
### 4. Top Accident-Prone Areas (All Vehicles)
1. **Wexford/Maryvale**
- 17,921 accidents | 18 fatalities
2. **West Humber-Clairville**
- 14,802 accidents | 17 fatalities
3. **St Lawrence East Bayfront**
- 10,258 accidents | 12 fatalities
### 5. Insurance Risk Metrics
- **Fatality Rate**: 0.08% (Fatal Accidents/Total Accidents)
- **Severe Injury Rate**: 0.14%
- **High-Risk Zones (Severity Scores)**:
1. Mount Dennis (21.33)
2. Eglinton East (19.67)
3. Humber Heights-Westmount (19.50)
- **Lowest-Risk Zones**:
1. North Toronto (8.51)
2. Henry Farm (8.70)