Ding, Fan (Monash University Malaysia), Tew, Hwa Hui (Monash University), Loo, Junn Yong (Monash University Malaysia), Susilawati, Susilawati (Monash University Malaysia), Liu, LiTong (Monash University), Leong, Fang Yu (Monash University), Luo, Xuewen (Monash University), CHIN, Kar Keong (Perunding Atur Trafik Sdn Bhd), GAN, Jia Jiun (Perunding Atur Trafik Sdn Bhd)

GSMT: Graph Fusion and Spatiotemporal Task Correction for Multi-Bus Trajectory Prediction

Scheduled for presentation during the Regular Session "S34a-Data-Driven Optimization and Governance in Intelligent Urban Mobility" (FR-LM-T34), Friday, November 21, 2025, 12:10−12:30, Surfers Paradise 1

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

Accurate trajectory prediction for buses is crucial in intelligent transportation systems, particularly within urban environments. In developing regions where access to multimodal data is limited, relying solely on onboard GPS data remains indispensable despite inherent challenges. To address this problem, we propose GSMT, a hybrid model that integrates a Graph Attention Network (GAT) with a sequence-to-sequence Recurrent Neural Network (RNN), and incorporates a task corrector capable of extracting complex behavioral patterns from large-scale trajectory data.The task corrector clusters historical trajectories to identify distinct motion patterns and fine-tunes the predictions generated by the GAT and RNN. Specifically, GSMT fuses dynamic bus information and static station information through embedded hybrid networks to perform trajectory prediction, and applies the task corrector for secondary refinement after the initial predictions are generated. This two-stage approach enables multi-node trajectory prediction among buses operating in dense urban traffic environments under complex conditions. Experiments conducted on a real-world dataset from Kuala Lumpur, Malaysia, demonstrate that our method significantly outperforms existing approaches, achieving superior performance in both short-term and long-term trajectory prediction tasks.