Accurate traffic forecasting is important for refining traffic management and planning to avoid congestion on the
roads and enhance road safety. Traditional models often misfire on complex, nonlinear patterns in traffic. In this study, the
hybrid LSTM-CNN model proposed in this paper would overcome the limitations by modeling both temporal and spatial
dependencies, thus ensuring better accuracy and reliability in prediction. The study portrays the hybrid model of LSTM-CNN to
overcome all such limitations and focus on capturing the temporal and spatial dependencies pertaining to traffic features. The
paper uses a rich dataset comprising variables like volume, speed, and occupancy from highway sensors. It gives a model using
LSTM layers in combination with CNN to perform better in prediction. Further refinements were done in training using
hyperparameters; the evaluation of performance was executed on R², MAPE, and RMSE. The hybrid model gave the lowest
validation loss of 0.05 and the lowest test MAPE of 0.08, which is better than the conventional models. More precisely, from the
LSTM model, R² score = 0.081, MAPE = 3.66%, and RMSE = 0.248; from the CNN model, R² score = 0.029, MAPE = 4.07%,
and RMSE = 0.255. R² of 0.063, MAPE of 3.84%, and RMSE of 0.250 were found for the hybrid model, with LSTM before CNN.
In reversed order—that is, the hybrid model of CNN first—the values are as follows: the model recorded an R² of 0.054, a MAPE
of 4.15%, and an RMSE of 0.252.
Keywords - Traffic forecasting, Hybrid LSTM-CNN model, Temporal and spatial dependencies, Prediction accuracy, Integration
of deep learning.