Evaluation of Microscopic Traffic Modeling using the Application of Artificial Intelligence

Effective traffic management is essential for addressing the critical problem of traffic congestion in urban areas, and the development of accurate and reliable traffic models plays an important role in this process. Microscopic traffic models that simulate individual vehicle behaviour have gained popularity in recent years. However, the development of these models can be challenging due to the complex interactions between vehicles and the environment. In response, artificial intelligence (AI) has emerged as a promising approach to traffic modelling. This paper covers the review of microscopic traffic models that use artificial intelligence (AI) techniques, such as modeling based on intelligent transport system, microscopic car-following and lane-changing models, and driver behaviour models. The review is divided into three sections, each section discusses several papers that propose new models, methodologies, or algorithms, and provides a brief overview of their contributions, methodology, and simulation results. Specifically, the paper discusses the advantages of AI-based traffic models, such as their ability to handle large datasets and complex traffic scenarios, improved accuracy and increased efficiency. Additionally, the paper highlights the limitations of AIbased traffic models, such as their reliance on the accuracy of the data that is used in developing the models, their computing intensity, and their inability to completely replace human drivers. Overall, this review highlights the potential of AI to revolutionize the field of traffic modelling and offers insights into the challenges and opportunities of AI-based traffic models