Urban Traffic Safety: A Review of Data Sources and Machine Learning Models for Vehicle Accident Risk Assessment

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This review examines machine learning models for vehicle accident risk assessment in urban traffic, suggesting improvements for smart cities.

MAIN FINDING: Machine learning models are critical for evaluating vehicle accident risks in urban environments.
KEY EVIDENCE: The review covers various data sources including sensor data and GPS for effective risk assessment.
APPROACH: It discusses multiple ML techniques, such as decision trees and deep learning models like GNNs.
SIGNIFICANCE: Insights aim to help urban planners develop safer and more efficient smart transportation systems.

Dinesh Yadav (2025) studied this question.

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This review examines machine learning models for vehicle accident risk assessment in urban traffic, suggesting improvements for smart cities.