Forecasting Road Traffic Volume Using Temporal-Only Time Series Models

Predicting road traffic in road networks is essential for alleviating congestion and enhancing traffic flow efficiency. This study presents a traffic volume prediction model trained using time-series data collected from loop detectors installed at three junctions in Istanbul, Turkey. The detectors record vehicle counts, providing the historical data used to train a residual LSTM model designed to forecast vehicle volumes 15 minutes into the future. The proposed model achieved an average mean absolute error of 0.26 across multiple loop detectors, demonstrating its effectiveness in accurately predicting short-term traffic volumes.