Abstract
Traffic speed prediction uses historical data to model traffic patterns and generates forecasts for future steps. As the number of vehicles surges significantly, traffic congestion could negatively affect the quality of life, human health, and the environment. Thus, finding a method providing accurate and robust forecasts for road users and traffic management is crucial. In traffic prediction, capturing both long and short-term patterns is necessary for forecasting precisely. However, some models only perform well in short-term modeling and vice versa. This paper aims to create a hybrid model, namely LSTM-SVR, to overcome the mentioned difficulty. The LSTM-SVR combines LSTM for modeling long-term dependencies with SVR for capturing short-term features. The experiments corroborate that the model outperforms popular selected baselines. The results also show the ability of the proposed model to capture peak hours and short-term patterns. This research provides a reliable forecasting tool for traffic engineers and new insights into hybrid models in traffic speed prediction.
Please download the dataset and place it in the repository. Link to dataset: https://drive.google.com/file/d/1U7gqIOYTS4I6YfpNfBOViqDrpu2dy0vx/view?usp=sharing