Multi-scale Fusion and Dynamic Adaptive Graph Bus Passenger Flow Prediction Model

doi:10.3778/j.issn.1673-9418.2305107

Abstract

Abstract: Bus passenger flow prediction is a crucial issue of public transportation planning and management. Though spatio-temporal graph convolution has shown promising results for subway passenger flow prediction, the existing spatial modeling methods based on graph convolution will bring huge spatial memory consumption for complex bus lines and larger-scale node data. Additionally, bus passenger flow is significantly influenced by immediate traffic conditions within a short time. To tackle these challenges, a multi-scale fusion and dynamic adaptive graph bus passenger flow prediction model (MFDAG) is presented. The proposed model effectively integrates passenger flow, time, and weekly information to enhance the feature dimension of the data. Moreover, it employs a dynamic adaptive graph method to learn the relationships between different stations. Furthermore, a multi-scale fusion propagation method is proposed to represent the complex spatial dependency relation, and a multi-scale convolution propagation method is designed to learn the multi-scale temporal dependency relation. The experiments are conducted by using two passenger flow datasets, and the results are compared with other traffic prediction methods. Experimental results demonstrate that the proposed bus passenger flow prediction method based on multi-scale fusion and dynamic adaptive graph exhibits higher prediction accuracy.

Key words: bus passenger flow prediction, graph sampling, dynamic adaptive graph, multi-scale fusion

摘要： 公交客流预测是公共交通规划和管理中的重要问题。虽然时空图卷积在地铁客流预测任务中获得了很好的预测效果，但是面对公交更复杂的线路、大规模的节点数据，现有的基于图卷积的空间建模方法将带来巨大的空间内存消耗。同时，公交客流量短时间范围内更可能受到瞬时交通状况的影响。为了解决这些挑战，提出了一种多尺度融合和动态自适应图的公交客流预测模型（MFDAG）。该模型融合客流、时刻和周信息以增加数据的特征维度，用动态自适应图的方法来学习不同站点之间的关系。进一步提出了一种多尺度融合传播的方法来表示复杂的空间依赖关系，同时设计了一种多尺度卷积传播的方法来学习不同尺度的时间依赖关系。在两个真实的客流数据集上进行了实验，并与其他交通预测方法进行了比较。实验结果表明，所提出的多尺度融合和动态自适应图的公交客流预测方法具有更高的预测准确度。

关键词: 公交客流预测, 图采样, 动态自适应图, 多尺度融合

GUO Xiangyu, PNEG Lilan, LI Chongshou, LI Tianrui. Multi-scale Fusion and Dynamic Adaptive Graph Bus Passenger Flow Prediction Model[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(7): 1879-1888.

郭翔宇, 彭莉兰, 李崇寿, 李天瑞. 多尺度融合与动态自适应图的公交客流预测模型[J]. 计算机科学与探索, 2024, 18(7): 1879-1888.

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