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Taxi drivers' traffic violations detection using random forest algorithm: A case study in China.

Ming Wan¹, Qian Wu¹, Lixin Yan¹

¹School of Transportation Engineering, East China Jiaotong University, Nanchang, China.

Traffic Injury Prevention

|March 28, 2023

Summary

This summary is machine-generated.

This study analyzed taxi driver traffic violations in Nanchang, China, identifying key factors like functional districts and road grade that influence violation severity. Findings aim to improve road safety management and reduce traffic incidents.

Keywords:

Random Forest SHAP Taxi drivers’ traffic violations imbalanced dataset impact factors

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Area of Science:

Traffic Safety and Management
Data Science and Machine Learning in Transportation

Background:

Traffic violations by taxi drivers pose significant risks to public safety.
Understanding the factors contributing to these violations is crucial for effective traffic management.
Existing research may lack comprehensive analysis of specific contributing elements in urban taxi services.

Purpose of the Study:

To investigate the key factors influencing taxi drivers' traffic violations.
To develop a predictive model for the severity of traffic violations.
To provide data-driven insights for traffic management departments to enhance road safety.

Main Methods:

Utilized a dataset of 43,458 electronic enforcement records of taxi driver violations in Nanchang, China (July 2020 - June 2021).
Employed the Balanced Bagging Classifier (BBC) to address dataset imbalance.
Developed a Random Forest model to predict violation severity and used the Shapley Additionality Explanation (SHAP) framework to analyze influencing factors.

Main Results:

The Random Forest model achieved high performance metrics (accuracy: 0.877, m_AUC: 0.976) for predicting violation severity, outperforming other algorithms.
The SHAP analysis identified functional districts (mean SHAP: 0.39), violation location (0.36), and road grade (0.26) as the most impactful factors.
The Balanced Bagging Classifier effectively reduced the imbalance ratio from 6.61% to 2.60%.

Conclusions:

Functional districts, violation location, and road grade are critical determinants of taxi driver traffic violations.
The findings offer a theoretical foundation for targeted interventions to reduce taxi driver violations.
This research supports improved road safety management strategies for urban transportation systems.