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Traffic violations analysis: Identifying risky areas and common violations.
El Mehdi Ben Laoula1, Omar Elfahim1, Marouane El Midaoui2
12IACS Laboratory, ENSET, University Hassan II of Casablanca, Mohammedia, Morocco.
Heliyon
|September 4, 2023
Summary
This study identifies high-risk traffic violation areas and common offenses like speeding. K-means clustering optimizes enforcement strategies to improve road safety and reduce accidents.
Area of Science:
- Public Health
- Transportation Safety
- Data Science
Background:
- Road traffic accidents are a significant public health concern, causing fatalities and economic losses.
- Effective road safety measures are crucial for reducing accident incidence and severity.
- Targeted enforcement strategies are needed to address prevalent traffic violations.
Purpose of the Study:
- To propose an incremental road safety strategy for prioritizing enforcement.
- To identify high-risk areas and common traffic violations.
- To optimize resource allocation for law enforcement agencies.
Main Methods:
- Analysis of traffic violation data across different districts.
- Comparison of district violation data against the overall average using the Kolmogorov-Smirnov (KS) test.
- Evaluation of various clustering optimization techniques, including k-means, to identify violation clusters.
Main Results:
- Identification of specific districts with elevated risks of traffic violations.
- Detection of common violations such as speeding, registration, license, seatbelt, driving under influence, and phone usage.
- K-means clustering demonstrated superior performance in identifying violation clusters for enforcement optimization.
Conclusions:
- Law enforcement can utilize these findings to focus on high-risk zones and prevalent violations.
- Optimized enforcement strategies can lead to more efficient resource utilization.
- The study provides a data-driven approach to enhance overall road safety and mitigate accident consequences.
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