Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System.

Area of Science:

• Intelligent Traffic Management and Control (ITMC)
• Urban Planning
• Data Science

Background:

• Urbanization presents complex traffic management challenges, including unpredictable dynamics and abnormal events.
• Intelligent Traffic Management and Control (ITMC) is crucial for efficient urban mobility, safety, and economic growth.
• Existing ITMC solutions often struggle with real-time forecasting and control under diverse traffic conditions.

Purpose of the Study:

• To review public datasets for developing ITMC models.
• To analyze deep learning approaches for traffic state forecasting and intersection signal control.
• To identify limitations and suggest improvements for robust ITMC systems.

Main Methods:

• Literature review of ITMC research, focusing on datasets and models.
• Analysis of deep learning techniques for short-term traffic state prediction.
• Evaluation of models for multi-intersection signal control.

Main Results:

• Deep learning models achieve reasonable results for short-term traffic forecasting and signal control.
• Current models lack robustness, especially during oversaturated traffic conditions.
• Public datasets are vital for developing and validating ITMC models.

Conclusions:

• Deep learning offers significant potential for ITMC but requires enhanced robustness for real-world application.
• Addressing unusual traffic scenarios, like oversaturation, is key to improving ITMC system reliability.
• Further research is needed to ensure safe and effective deployment of advanced ITMC models.