Hierarchical graph construction for station-free traffic demand prediction

Jinyan Hou, Shan Liu, Ya Zhang and Haotong Qin

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 199, issue C

Abstract: Graph neural networks (GNNs) have been widely applied in traffic demand prediction, and traffic modes can be divided into station-based modes, such as buses and subways, and station-free modes, such as ride-hailing and bike-sharing. Existing research in traffic graph construction primarily relies on the distribution of actual traffic stations to build graphs based on the road network. However, station-free traffic demand prediction cannot utilize real traffic stations, and the complexity and heterogeneous distribution of data further introduce significant challenges. As a result, effectively leveraging this information for constructing graph structures has become a major challenge. To tackle these, this paper introduces a novel hierarchical graph construction method tailored to station-free traffic mode. We propose a novel density-based clustering algorithm (HDPC-L) to determine the flexible positioning of nodes in the graph, overcoming the computational bottlenecks of traditional clustering algorithms and enabling effective handling of large-scale datasets. Furthermore, we extract valuable information from ridership data to initialize the edge weights of GNNs. Comprehensive experiments on two real-world datasets, the Shenzhen bike-sharing dataset and the Haikou ride-hailing dataset, show that the method significantly improves the performance of the model. On average, our models show an improvement in accuracy of around 17.74% and 13.41% on the two datasets. Additionally, it significantly enhances computational efficiency, reducing training time by approximately 7.64% and 27.16% on the two datasets. We make our code available at https://github.com/houjinyan/HDPC-L-ODInit.

Keywords: Graph construction; Graph neural network; Station-free traffic mode; Traffic demand prediction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104151

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