基于异构时空图卷积网络的出租车客流预测  

Taxi Passenger Flow Prediction Based on Heterogeneous Spatiotemporal Graph Convolutional Networks

作　　者：李桃迎 李蒙 武梦乔 LI Taoying;LI Meng;WU Mengqiao(School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,China)

机构地区：[1]大连海事大学航运经济与管理学院,辽宁大连116026

出　　处：《计算机与现代化》2024年第11期13-18,共6页Computer and Modernization

基　　金：教育部人文社科基金资助项目(21YJC630066);国家自然科学基金资助项目(51939001);辽宁省兴辽英才计划项目(XLYC1907084);辽宁省重点研发计划项目(2020JH2/10100042)。

摘　　要：准确预测区域级出租车客流量对出租车调度和乘客出行至关重要。而挖掘客流的时空相关性则是提高预测精度的关键。针对现有研究中对区域客流的时空特征挖掘不充分,特别是非邻近区域间客流相似性以及区域之间的空间关系没有充分挖掘的问题,本文提出一种考虑时空相似性的异构时空图卷积网络,以实现对区域级客流预测。该模型以区域物理邻接图、区域相似图和OD(出发-到达)关联图构建异构图,以表达客流数据的时空特性。在公开数据集上进行实验,结果表明:该模型在平均绝对误差、均方根误差、准确率和R2方面均优于对比模型,具有更高的预测精度。Accurately predicting regional taxi passenger flow plays an important role in taxi dispatch and passenger transportation.The exploration of spatiotemporal correlations in passenger flow is a critical factor in enhancing prediction accuracy.In light of the limited investigation into the spatiotemporal characteristics of regional passenger flow,particularly the inadequate exploration of passenger flow similarities between non-adjacent regions and the underexplored spatial relationships among regions,a Heterogeneous Spatio-Temporal Graph Convolutional Network(HSTGCN)is proposed to predict the passenger flow across multiple target regions.To capture the spatiotemporal characteristics of passenger flow data,we construct a heterogeneous graph utilizing regional physical adjacency graphs,regional similarity graphs,and origin-destination(OD)correlation graphs.Furthermore,based on these adjacency matrices,we build a dynamic graph reflecting the spatiotemporal dynamics of regions.We employ heterogeneous spatiotemporal graph convolutional networks to extract the spatiotemporal features of the data.Experimental results on publicly available datasets demonstrate that the model’s prediction outcomes outperform comparative models in terms of mean absolute error,root mean square error,accuracy,and R2,showcasing superior prediction accuracy.

关 键 词：出租车调度 客流预测 时空特征 图卷积网络 门控递归单元 

分 类 号：U491.14[交通运输工程—交通运输规划与管理]