A Machine Learning-Based CNN–Bilstm Framework for Traffic Congestion and Travel Time Prediction
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The main issues facing modern metropolitan transportation systems are traffic bottlenecks and variations in travel times. They impede mobility, damage the environment, and compromise commute safety. Intelligent transportation systems and smart city applications need precise estimations of traffic congestion and journey durations in the short term to function effectively. This study introduces a hybrid deep learning framework that employs a Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture for simultaneous forecasting of traffic congestion and travel time. The model employs CNN layers to acquire bidirectional temporal relationships and BiLSTM units to capture spatial correlations across adjacent road segments. The framework just employs fundamental traffic information, such as volume, speed, and flow, enhancing its practicality for real-world applications since it does not need additional data sources.
We used traffic data from the Delhi–Meerut Expressway to evaluate the proposed technique. An extensive exploratory data analysis was conducted to understand traffic patterns and confirm the spatial-temporal modeling methodology. Experimental findings demonstrate persistent model convergence and enhanced prediction accuracy, achieving a low Mean Squared Error (0.0158), Root Mean Squared Error (0.1257), Mean Absolute Error (0.0912), and a Mean Absolute Percentage Error of around 4.6%. Visual comparisons indicate that forecast traffic numbers closely align with actual patterns, and an analysis of error distribution demonstrates that the model is very effective in the presence of noisy data. The model provides comprehensible outputs, including congestion level, anticipated speed, and travel duration, enhancing its practical use. The proposed CNN–BiLSTM architecture is an effective, robust, and scalable method for real-time prediction of traffic congestion and travel durations in urban areas.
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