计算机科学与探索2024，Vol.18Issue(7)：1879-1888,10.DOI:10.3778/j.issn.1673-9418.2305107
多尺度融合与动态自适应图的公交客流预测模型
Multi-scale Fusion and Dynamic Adaptive Graph Bus Passenger Flow Prediction Model
摘要
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 pas-senger 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.关键词
公交客流预测/图采样/动态自适应图/多尺度融合Key words
bus passenger flow prediction/graph sampling/dynamic adaptive graph/multi-scale fusion分类
计算机与自动化引用本文复制引用
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):1879-1888,10.基金项目
国家自然科学基金(62202395,62176221) （62202395,62176221）
四川省自然科学基金(2022NSFSC0930) （2022NSFSC0930）
中央高校基本科研业务费专项资金(2682022CX067) （2682022CX067）
四川省科技计划(MZGC20230073).This work was supported by the National Natural Science Foundation of China(62202395,62176221),the Natural Science Foundation of Sichuan Province(2022NSFSC0930),the Fundamental Research Funds for the Central Universities of China(2682022CX067),and the Science and Technology Plan of Sichuan Province(MZGC20230073). （MZGC20230073）
