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Advancing passenger next-station prediction via collaborative knowledge graph representational learning
Xiaoqi Duan1, Jianlong Wang2, Zhibang Xu3
1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
Scientific Reports
|June 15, 2026
Summary
This study introduces a new method combining reinforcement learning and knowledge graphs for better passenger next-station prediction. The approach enhances understanding of travel patterns, significantly improving prediction accuracy for stations, routes, and distances.
Area of Science:
- Transportation Science
- Artificial Intelligence
- Data Science
Background:
- Conventional next-station prediction models struggle with dynamic passenger-station interactions due to rigid graph structures.
- Existing methods lack sufficient representation of complex travel patterns and associated knowledge.
Purpose of the Study:
- To develop a novel approach for next-station prediction by integrating reinforcement learning with knowledge graphs.
- To enhance the representation of passenger-station interactions and travel patterns using heterogeneous data fusion.
Main Methods:
- Utilized a reinforcement learning framework enriched with environmental variables.
- Introduced collaborative updating mechanisms based on human travel knowledge graphs to model passenger-station interactions.
- Employed enhanced representations for improved next-station prediction accuracy.
Main Results:
- The proposed method significantly outperforms classical algorithms in predicting next-stations, routes, and travel distances.
- Demonstrated superior efficacy in capturing complex passenger travel behaviors compared to existing models.
- Ablation experiments and comparative analyses validated the effectiveness of the integrated approach.
Conclusions:
- The integration of reinforcement learning and knowledge graphs offers a holistic approach to passenger travel behavior modeling.
- The proposed method provides a more accurate and comprehensive solution for next-station prediction in public transportation.
- This research establishes a new benchmark for understanding and predicting intricate passenger mobility patterns.
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