Na, Chaemin (Korea Advanced Institute of Science and Technology), Kim, Hyunsoo (Korea Advanced Institute of Science and Technology), Yeo, Hwasoo (KAIST)

Turn-Level Maximum Queue Length Prediction Based on Dynamic Graph Deep Learning Considering Traffic Signal

Scheduled for presentation during the Regular Session "S30a-Intelligent Modeling and Prediction of Traffic Dynamics" (TH-LM-T30), Thursday, November 20, 2025, 11:10−11:30, Gold Coast

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

As urbanization accelerates and vehicle volumes grow, turn-level queue length prediction at signalized intersections becomes crucial for traffic management. This study proposes a dynamic graph deep learning model that incorporates traffic signal states to predict maximum turn-level queue lengths. A simulation-based dataset was created by varying network structure, traffic demand, and signal plans. The data were represented as dynamic graphs with time-varying adjacency matrices based on traffic signal states. A Graph Attention Network (GAT) was used to capture spatial dependencies, followed by a Gated Recurrent Unit (GRU) for temporal modeling. A congestion-aware weighted loss function was introduced to improve accuracy under heavy traffic. The model’s performance was evaluated against various baselines and signal representations using MAE and RMSE. Results show that the proposed model outperforms existing methods, even in extended prediction horizons. These findings highlight the potential of dynamic graph learning for detailed turn-level traffic prediction for traffic management.