The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

    本发明公开了一种基于时空特征深度融合的全周期交通流量预测方法,旨在深度整合交通流的复杂时空依赖,形成复合交通态势感知,实现对交通多周期综合流量预测。首先通过时空信息综合表征,提取多时间模态的时序特征并构建具象的真实空间关系。为进一步构建融合多尺度以及多粒度综合要素的数字化城市交通流量预测方法,本发明针对短期交通流量预测提出深度融合短期时空依赖的综合交通流量预测模型,提高对瞬时流量变化的敏感性,充分建模交通流量在多功能空域节点的短期动态变化;此外,还设计了一种深度空域解构与时序特征融合的模型用于中长期交通预测,深层次提取复杂交通模式。仿真结果显示,本发明所提方法优于现有交通流量预测技术。


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Full-period traffic flow prediction method based on spatial-temporal feature deep fusion


    Weitere Titelangaben:

    一种基于时空特征深度融合的全周期交通流量预测方法


    Beteiligte:
    ZHAO ZHONGNAN (Autor:in) / XIE XU (Autor:in) / WANG YUE (Autor:in)

    Erscheinungsdatum :

    28.02.2025


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Traffic flow prediction method and device based on local-global spatial-temporal feature fusion

    ZONG XINLU / CHEN ZHEN / WANG CHUNZHI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Traffic flow prediction method and system based on non-embedded spatial-temporal feature fusion

    ZHENG JIANYING / LUO YONG / CHEN QIAN | Europäisches Patentamt | 2024

    Freier Zugriff

    Spatial-temporal feature fusion traffic flow prediction method and device, equipment and storage medium

    SHEN QING / ZHANG XIANGZHENG / LOU JUNGANG et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Traffic flow prediction method based on multivariate spatial-temporal feature extraction

    WANG XING / WANG XIAOJUN / HUANG FALIANG et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    Spatial-Temporal Multiscale Fusion Graph Neural Network for Traffic Flow Prediction

    Hou, Hongxin / Ning, Nianwen / Shi, Huaguang et al. | IEEE | 2022