Leveraging AI for Traffic Prediction and Optimization in Urban Environments
DOI:
https://doi.org/10.26438/ijcse/v13i11.5360Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Traffic Prediction Traffic Optimization, Urban Mobility, Smart Cities, Intelligent TransportationAbstract
The escalating challenges of urban traffic congestion, encompassing economic losses, environmental degradation, and diminished quality of life, necessitate innovative solutions beyond traditional traffic management paradigms. This survey paper provides a comprehensive review of the application of Artificial Intelligence (AI) techniques in tackling the complexities of traffic prediction and optimization within urban environments. We delve into various AI methodologies, including classical machine learning, deep learning architectures (such as Convolutional Neural Networks, Recurrent Neural Networks, and Graph Neural Networks), and reinforcement learning, highlighting their unique strengths in processing heterogeneous traffic data and addressing dynamic urban mobility patterns. The paper discusses how these AI approaches are leveraged for short-term and long-term traffic forecasting, real-time congestion management, adaptive traffic signal control, intelligent route guidance, and public transport optimization. Furthermore, we identify current challenges, including data quality, computational demands, model interpretability, and generalizability, while proposing promising future research directions, such as hybrid AI models, explainable AI, digital twins, and the integration with emerging vehicle-to-everything (V2X) communication technologies. This survey aims to serve as a valuable resource for researchers and practitioners interested in advancing smart city initiatives through AI-driven traffic solutions.
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