Abstract

This paper investigates how Machine Learning (ML) models can be used in traffic surveillance and predict accidents in the United Arab Emirates (UAE). In spite of large infrastructure developments and stringent traffic laws, traffic accidents continue to be a major problem with an increase in fatalities and injuries. There is a gap in effective detection and responses since traditional monitoring and forecasting techniques are unable to capture the intricate, nonlinear, and time-dependent character of traffic patterns. This thesis fills that void by utilizing sophisticated the potential use of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) models to produce better predictors becomes relevant with the everincreasing number of traffic-related accidents and further demand of intelligent transportation systems. A real-world traffic accident data set that was acquired from MOI (Ministry of Interior) located in UAE. These open-source datasets obtained more than 6,400 accident records which was examined, including such characteristics as type, weather, questionable condition of the road, lighting, time, and the extent of injuries. Following the pre-processing, training, testing, and comparing models were done in terms of metrics of accuracy, training time, and loss convergence. Five Python (TensorFlow/Keras) ML models were constructed and assessed using the same training/testing splits According to the results, RNN had the highest accuracy (~91%) and was able to capture time-dependent variables such daily or seasonal variations. CNN, which has a great feature-learning capability, came in second (around 90%). ANN and DNN were effective for batch analytics, achieving about 88% with reduced training periods. The lack of a real-time simulation framework caused RL to score the worst (~76%) despite its theoretical promise for adaptable decision-making. Time, weather, illumination, and accident type were the most significant indicators, according to SHAP analysis.

Library of Congress Subject Headings

Traffic accidents--United Arab Emirates--Forecasting--Automation; Traffic safety--United Arab Emirates--Data processing; Machine learning; Neural networks (Computer science)

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Khalil Alhussaeni

Campus

RIT Dubai

Plan Codes

PROFST-MS

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