Adaptive data processing framework for efficient short-term traffic flow prediction

被引：0
|
作者：
Li, Zonghan [1 ]
Wei, Yangbo [2 ]
Zhang, Yixian [1 ]
Zhao, Xuan [1 ]
Cao, Jinde [1 ,3 ]
Guo, Jianhua [4 ]
机构：
[1] Southeast Univ, Sch Math, Nanjing 211189, Peoples R China
[2] Southeast Univ, Sch Phys, Nanjing, Peoples R China
[3] Ahlia Univ, Manama 10878, Bahrain
[4] Southeast Univ, Intelligent Transportat Syst Res Ctr, Nanjing 21009, Peoples R China
来源：
NONLINEAR DYNAMICS | 2024年 / 112卷 / 17期
基金：
中国国家自然科学基金;
关键词：
Adaptive traffic flow sequence; Short-term traffic forecasting; Unsupervised learning; Gated recursive unit; Attention mechanism; MODEL; LOAD; LSTM; SVR; GRU;
D O I：
10.1007/s11071-024-09844-w
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Accurate short-term traffic forecasting is a prerequisite for establishing intelligent transportation systems. In this paper, a new adaptive traffic flow sequence framework is processed. For any given dataset, the framework divides the original sequence into different time periods, generating an adaptive traffic flow sequence. Compared with the sequence using fixed aggregation intervals, adaptive traffic flow data is reduced by 33% on average. Subsequently, local and global prediction models are presented for adaptive traffic flow sequences. Namely, the data-extended GRU algorithm and data-adaptive Bi-GRU algorithm respectively demonstrate best mean absolute percentage error of 4.70% and 6.80% according to the data experiments.
引用
收藏
页码：15231 / 15249
页数：19
相关论文
共 50 条
  • [1] A Short-Term Traffic Flow Combination Prediction Model with Adaptive Weights
    Sun, Chuanxia
    Sun, Xiaoliang
    Yin, Peixuan
    Zhang, Shenyuan
    [J]. CICTP 2020: ADVANCED TRANSPORTATION TECHNOLOGIES AND DEVELOPMENT-ENHANCING CONNECTIONS, 2020, : 324 - 331
  • [2] An adaptive hybrid model for short-term urban traffic flow prediction
    Hou, Qinzhong
    Leng, Junqiang
    Ma, Guosheng
    Liu, Weiyi
    Cheng, Yuxing
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 527
  • [3] Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN
    Qi D.
    Mao Z.
    [J]. Journal of Geo-Information Science, 2022, 24 (02) : 339 - 351
  • [4] A Domain Adaptation Framework for Short-term Traffic Prediction
    Li, Junyi
    Zhang, Kaihang
    Shen, Loutao
    Wang, Zhebing
    Guo, Fangce
    Angeloudis, Panagiotis
    Chen, Xiqun
    Hu, Simon
    [J]. 2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2021, : 3564 - 3569
  • [5] Research on short-term Traffic flow Prediction Based on Big Data Environment
    Li, Yutao
    Jiang, Wengang
    [J]. 2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 1758 - 1762
  • [6] Short-term Traffic Flow and Platoon Prediction Based on the SVM with the Combined Data
    Lei, Wen
    Chen, Fu Yang
    Jiang, Bin
    Wang, Li
    [J]. FRONTIERS OF MANUFACTURING SCIENCE AND MEASURING TECHNOLOGY V, 2015, : 230 - 234
  • [7] Data driven hybrid fuzzy model for short-term traffic flow prediction
    Li, Chengdong
    Yan, Bingyang
    Tang, Minjia
    Yi, Jianqiang
    Zhang, Xiqiao
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (06) : 6525 - 6536
  • [8] Analysis of spectrum and prediction for short-term traffic flow
    Weng Xiaoxiong
    Jian Jun
    [J]. PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 6, 2007, : 58 - +
  • [9] A Hybrid Method for Short-Term Traffic Flow Prediction
    Song, Wei
    Yin, Taolin
    [J]. 2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2020, : 496 - 499
  • [10] Short-term Traffic Flow Prediction Based on ANFIS
    Chen Bao-ping
    Ma Zeng-qiang
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMMUNICATION SOFTWARE AND NETWORKS, 2009, : 791 - +
12345