COMPARATIVE OF LSTM AND GRU FOR TRAFFIC PREDICTION AT ADIPURA INTERSECTION, BANDAR LAMPUNG - Ilham Firman Ashari; Verlina Agustine; Aidil Afriansyah; Nela Agustin Kurnianingsih; Andre Febrianto; Eko Dwi Nugroho (LPPM Nusa Mandiri, 2025)
The Tugu Adipura intersection in Bandar Lampung is a vital traffic hub connecting four major roads. Rapid population growth and increasing vehicle numbers challenge traffic flow and urban quality of life. Despite its importance, there is limited research using predictive models to analyze traffic patterns at complex intersections in mid-sized Indonesian cities. This study addresses that gap by examining traffic growth on four connected roads using deep learning models. Traffic data were collected hourly from June 1, 2021, to July 31, 2023. A comparative analysis of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models was conducted, with SGD and Adam as optimizers. Results show the GRU model with Adam achieved the lowest RMSE (0.23) on road section 1, indicating its superior ability to model short-term fluctuations and non-linear growth in traffic volume. The study offers practical implications for traffic management by highlighting GRU’s capacity to capture seasonal trends and rapid growth, supporting proactive infrastructure planning and congestion mitigation strategies. These findings demonstrate the value of data-driven approaches in enhancing transportation systems in growing urban areas.