Paper Contents
Abstract
Road transport infrastructure is the backbone of contemporary mobility and logistics, but it remains significantly prone to accidents on the roads and traffic congestion, resulting in tremendous economic, environmental, and human losses. While traditional road traffic management systems fail to cope with increasing urbanization and dynamic traffic patterns, Artificial Intelligence (AI) presents a potential path towards intelligent, real-time, and data-driven solutions. This study suggests an AI-driven predictive modeling system for accident prevention and traffic management on highways. The fundamental goal is to create machine learning algorithms that can predict traffic accidents prior to occurrence and optimize traffic flow in real time to reduce congestion and enhance road safety.The research starts with a thorough review of literature to gauge the existing scenario of AI usage in transport, with special emphasis on accident detection, severity estimation, and traffic flow optimization. The research points out some shortcomings in current systems such as low predictive efficiency, inadequate real-time adaptability, and restricted scalability over geographical locations. To solve these challenges, the suggested framework combines diverse sources of data, such as real-time traffic sensor data, GPS trajectories, historical crash records, weather conditions, and CCTV recordings.We utilize and compare various AI models, such as Random Forest, Gradient Boosting, Support Vector Machines, and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units, to predict the likelihood of accidents from spatiotemporal attributes. Concurrently, reinforcement learning (RL) is investigated to adaptively control traffic signals to alleviate jams in accident hotspots. Test metrics like accuracy, precision, recall, F1-score, and Root Mean Square Error (RMSE) are employed to measure the performance of the models. It is shown through our experiments that ensemble models (more notably XGBoost and Random Forest) perform better than conventional statistical models for accident prediction with accuracies greater than 90%. LSTM-based deep learning models are also shown to perform well in temporal sequence prediction under varying traffic and weather conditions.The traffic control module, which was trained with Q-learning and Deep Q-Networks (DQN), is effective in minimizing traffic delay times and maximizing vehicle throughput by real-time adaptation of signal timing. Also, a simulation based on SUMO (Simulation of Urban Mobility) confirms the end-to-end efficiency of the proposed system.The findings of this research underscore the revolutionary value of AI in ensuring highway networks are safer and more efficient. Through timely warnings to drivers and real-time instructions to traffic management centers, the system significantly contributes to advanced accident prevention. Additionally, it facilitates adaptive measures for adapting to dynamic traffic flow, taking smart cities a step closer to achieving the objective of zero deaths and effortless mobility.This study not only advances scholarly understanding in the field of AI and transportation engineering but also plays a practical role in the toolset of several stakeholders: policymakers, urban planners, and ITS developers. Future work will involve field deployment with traffic authorities, improving the modelsrobustness based on edge computing and 5G integration, and identifying ethical issues related to surveillance and data privacy in AI-based traffic systems.
Copyright
Copyright © 2025 Jeet Mahata . This is an open access article distributed under the Creative Commons Attribution License.
