Dynamic Graph Representation Learning for Passenger Behavior Prediction

Mingxuan Xie, Tao Zou, Junchen Ye (), Bowen Du and Runhe Huang
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Mingxuan Xie: CCSE Lab, Beihang University, Beijing 100191, China
Tao Zou: CCSE Lab, Beihang University, Beijing 100191, China
Junchen Ye: School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Bowen Du: CCSE Lab, Beihang University, Beijing 100191, China
Runhe Huang: Faculty of Computer and Information Science, Hosei University, Tokyo 102-8160, Japan

Future Internet, 2024, vol. 16, issue 8, 1-17

Abstract: Passenger behavior prediction aims to track passenger travel patterns through historical boarding and alighting data, enabling the analysis of urban station passenger flow and timely risk management. This is crucial for smart city development and public transportation planning. Existing research primarily relies on statistical methods and sequential models to learn from individual historical interactions, which ignores the correlations between passengers and stations. To address these issues, this paper proposes DyGPP, which leverages dynamic graphs to capture the intricate evolution of passenger behavior. First, we formalize passengers and stations as heterogeneous vertices in a dynamic graph, with connections between vertices representing interactions between passengers and stations. Then, we sample the historical interaction sequences for passengers and stations separately. We capture the temporal patterns from individual sequences and correlate the temporal behavior between the two sequences. Finally, we use an MLP-based encoder to learn the temporal patterns in the interactions and generate real-time representations of passengers and stations. Experiments on real-world datasets confirmed that DyGPP outperformed current models in the behavior prediction task, demonstrating the superiority of our model.

Keywords: traffic data mining; passenger behavior prediction; graph neural networks; dynamic graph learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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