SPATIO-TEMPORAL TRAFFIC CONGESTION FORECASTING IN TASHKENT CITY USING A CNN-LSTM DEEP LEARNING MODEL BASED ON GOOGLE MAPS AND WEATHER DATA (INTERNATIONAL SCIENTIFIC-ELECTRONIC JOURNAL “PIONEERING STUDIES AND THEORIES”, 2025)
Traffic congestion remains a significant challenge in rapidly urbanizing cities like Tashkent, where increasing vehicle usage strains existing infrastructure. This paper presents a spatiotemporal traffic forecasting framework based on a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The  model leverages real-time traffic data collected from Google Maps and hourly weather data from OpenWeatherMap to predict short-term congestion levels across key urban road segments. By capturing both spatial patterns and temporal dependencies, the CNN-LSTM model effectively accounts for dynamic conditions such as time of day, weather variability, and traffic flow trends. Experimental results demonstrate that the proposed model achieves high prediction accuracy, with low mean absolute error and strong generalization across different conditions. This research contributes a practical and scalable approach to intelligent traffic management and urban mobility planning in Tashkent and similar cities.