计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (7): 98-106.doi: 10.11896/jsjkx.220900109

• 数据库&大数据&数据科学 • 上一篇    下一篇

探索站点时空移动模式:长短期交通预测框架

沈哲辉1, 王开来2, 孔祥杰1   

  1. 1 浙江工业大学计算机科学与技术学院 杭州 310023
    2 大连理工大学软件学院 大连 116620
  • 收稿日期:2022-09-13 修回日期:2022-12-01 出版日期:2023-07-15 发布日期:2023-07-05
  • 通讯作者: 孔祥杰(xjkong@ieee.org)
  • 作者简介:(shenzhehui198@outlook.com)
  • 基金资助:
    国家自然科学基金(62072409);浙江省自然科学基金(LR21F020003)

Exploring Station Spatio-Temporal Mobility Pattern:A Short and Long-term Traffic Prediction Framework

SHEN Zhehui1, WANG Kailai2, KONG Xiangjie1   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 School of Software,Dalian University of Technology,Dalian,Liaoning 116620,China
  • Received:2022-09-13 Revised:2022-12-01 Online:2023-07-15 Published:2023-07-05
  • About author:SHEN Zhehui,born in 1999,postgra-duate.His main research interests include urban science,social computing and so on.KONG Xiangjie,born in 1981,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include network science,mobile computing and computational social science.
  • Supported by:
    National Natural Science Foundation of China(62072409) and Natural Science Foundation of Zhejiang Province,China(LR21F020003).

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摘要： 随着智慧城市系统的技术发展与城市时空数据的急剧增加,公共服务需求也日益受到重视。公共交通作为城市交通中至关重要的组成部分,同样面临着巨大的挑战,并且交通网络的时空预测任务往往是解决各种交通问题的核心一环。交通中的移动模式可以体现城市人群的出行行为及其规律,大多数交通预测任务研究中,移动模式的重要性经常被忽视。针对现有工作的问题,提出了一种多模式的交通预测框架(MPGNNFormer),使用基于图神经网络的深度聚类的方法提取站点的移动模式,并设计了一种基于Transformer的时空预测模型,在充分利用时间依赖关系和空间依赖关系的同时,提高了计算效率。在现实的公交车数据集上展开了一系列实验以进行评估和测试,包括移动模式的分析和预测结果对比,实验结果证明了所提方法在交通网络的长短期交通预测上的有效性。最后讨论了所提方法可扩展性。

关键词: 时空数据挖掘, 长短期交通预测, 移动模式, 深度学习

Abstract: With the technological development of intelligent transportation system and the surging spatio-temporal data in urban,the demand for public services is increasingly emphasized.As a vital part of urban transportation,public transportation also faces enormous challenges,and the spatio-temporal prediction task in transportation network is the core of the solutions for various traffic problems.Mobility pattern in traffic can reflect the travel behaviors of people and their rules.In most studies on traffic prediction task,the importance of mobility pattern is neglected.In view of the problem of existing work,a multi-pattern traffic prediction framework,MPGNNFormer,is proposed,in which based-graph neural network deep clustering method is used to extract mobility patterns of stations,and a Transformer-based spatio-temporal prediction model is designed to learn temporal dependence and spatial dependence of stations and to improve the computational efficiency.Then,a series of experiments are conducted on real bus dataset for evaluation and testing,including analysis of mobility patterns and comparison of prediction results.Finally,experimental results prove the efficacy of proposed method in the short and long-term traffic prediction of traffic networks,and its sca-lability is discussed.

Key words: Spatio-Temporal data mining, Short and long-term traffic prediction, Mobility pattern, Deep learning

中图分类号: 

  • TP183
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