Abstract
Rapid urbanization has exacerbated traffic congestion and associated vehicle emissions, making real-time air quality index (AQI) prediction crucial for urban environmental management. Transportation emissions, including exhaust gases and particulate matter, are the main factors causing urban environmental pollution. Vehicle emission-induced air pollution related to transportation affects public health, quality of life, and well-being on a global scale and impacts socioeconomic development and people’s livelihoods. The air quality index (AQI) is a comprehensive indicator reflecting the degree of air pollution. Understanding the pollution level in a specific area can help decision-makers manage traffic flow, reduce congestion and emissions, and improve traffic efficiency and environmental sustainability. Traditional prediction methods often have problems such as low accuracy and an inability to effectively handle complex data. Therefore, this paper explores a traffic air quality index prediction model based on the sparrow search algorithm (SSA)–variational mode decomposition (VMD)–gated recurrent unit algorithm (GRU) model, based in deep learning. Experimental results on real-world datasets demonstrate that the SSA-VMD-GRU model reduces the mean absolute percentage error (MAPE) by approximately 8% compared to the standalone GRU model, offering a robust solution for real-time AQI forecasting and practical insights for current urban traffic air quality index monitoring methods.