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Short-term traffic flow prediction is a critical component of efficient management in Intelligent Transportation Systems (ITS), providing real-time travel guidance for commuters and supporting informed decision-making by transportation authorities. To address the current challenges of insufficient prediction accuracy and excessive reliance on time-series features, we propose a spatio-temporal feature-integrated short-term traffic flow prediction model named KAN-CNN-BiLSTM. In this model, traffic flow data from the target road segment and its two adjacent segments are jointly fed into the model to fully leverage spatio-temporal features for prediction. Subsequently, a Convolutional Neural Network (CNN) extracts spatial features from the combined traffic flow data. To overcome the limitation of traditional LSTMs, which can only process unidirectional time series, we introduce a bidirectional long short-term memory network (BiLSTM) with symmetric time series extraction capability. This enables simultaneous capture of historical and future traffic flow dependencies. Finally, we replace the conventional fully connected network with a Kolmogorov–Arnold network (KAN) to enhance the representation of complex nonlinear features. Experimental results using traffic flow data from the UK Highways Agency website demonstrate that the KAN-CNN-BiLSTM model outperforms existing mainstream methods, achieving superior prediction accuracy and minimal error. The model’s MAE, RMSE, MAPE, and R2 values are 27.4696, 40.3923, 8.65%, and 0.9615, respectively.
Yang et al. (Mon,) studied this question.