Fast autoregressive tensor decomposition for online real-time traffic flow prediction

被引:14
|
作者
Xu, Zhihao [1 ,2 ]
Chu, Benjia [1 ,2 ]
Li, Jianbo [1 ,2 ]
Lv, Zhiqiang [1 ,2 ]
机构
[1] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266070, Peoples R China
[2] Qingdao Univ, Inst Ubiquitous Networks & Urban Comp, Qingdao 266071, Peoples R China
基金
中国国家自然科学基金;
关键词
Fast autoregressive tensor decomposition; Online real-time traffic flow prediction; Tucker Decomposition; Core tensor;
D O I
10.1016/j.knosys.2023.111125
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Online real-time traffic flow prediction typically offers better real-time performance than offline prediction. However, existing studies rarely discussed online real-time traffic flow prediction and the balance between prediction accuracy and computational costs. Training and predicting traffic flow data with complex patterns by artificial intelligence models is usually time-consuming. Some high-accuracy statistical learning methods also reach a performance bottleneck in terms of computational speed. Therefore, a Fast Autoregressive Tensor Decomposition (FATD) algorithm is proposed for online real-time traffic flow prediction. First, the historical tensor data is decomposed by Tucker Decomposition into factor matrices and easy-to-compute core tensors, and these core tensors are modeled by Tensor Seasonal Autoregressive Integrated Moving Average (Tensor SARIMA). Second, a future core tensor is predicted by Tensor SARIMA. By the Inverse Tucker Decomposition, the traffic flow data to be predicted is recovered. The experimental results show that the FATD algorithm can reduce the computational cost while maintaining high accuracy. Compared with baselines, the FATD algorithm reduces MAE, RMSE, and the computational cost by approximately 35.04%, 26.86%, and 99.28% on average, respectively.
引用
收藏
页数:18
相关论文
共 50 条
  • [11] Dynamics of traffic flow with real-time traffic information
    Yokoya, Y
    PHYSICAL REVIEW E, 2004, 69 (01): : 11
  • [12] 3D Markov Process for Traffic Flow Prediction in Real-Time
    Ko, Eunjeong
    Ahn, Jinyoung
    Kim, Eun Yi
    SENSORS, 2016, 16 (02):
  • [13] Real-Time Multistep Prediction of Sewer Flow for Online Chemical Dosing Control
    Chen, Jindong
    Ganigue, Ramon
    Liu, Yiqi
    Yuan, Zhiguo
    JOURNAL OF ENVIRONMENTAL ENGINEERING, 2014, 140 (11)
  • [14] Real-time Failure Prediction in Online Services
    Shatnawi, Mohammed
    Hefeeda, Mohamed
    2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (INFOCOM), 2015,
  • [15] A Bus Arrival Time Prediction Method Based on GPS position and Real-time Traffic Flow
    Lei, Jianmei
    Chen, Dongmei
    Li, Fengxi
    Han, Qingwen
    Chen, Siru
    Zeng, Lingqiu
    Chen, Min
    2017 IEEE 15TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 15TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 3RD INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS(DASC/PICOM/DATACOM/CYBERSCI, 2017, : 178 - 184
  • [16] A Fast Spatial-temporal Information Compression algorithm for online real-time forecasting of traffic flow with complex nonlinear patterns
    Xu, Zhihao
    Lv, Zhiqiang
    Chu, Benjia
    Li, Jianbo
    CHAOS SOLITONS & FRACTALS, 2024, 182
  • [17] Fast, accurate, and lightweight real-time traffic identification method based on flow statistics
    Tai, Masaki
    Ata, Shingo
    Oka, Ikuo
    PASSIVE AND ACTIVE NETWORK MEASUREMENT, PROCEEDINGS, 2007, 4427 : 255 - +
  • [18] The system for predicting the traffic flow with the real-time traffic information
    Cho, Mi-Gyung
    Yu, Young Jung
    Kim, SungSoo
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2006, PT 1, 2006, 3980 : 904 - 913
  • [19] Massive MIMO Channel Prediction in Real Propagation Environments Using Tensor Decomposition and Autoregressive Models
    Liu, Weihang
    Chen, Ziyu
    Gao, Xiang
    2022 IEEE 33RD ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (IEEE PIMRC), 2022, : 849 - 855
  • [20] Bus travel time prediction with real-time traffic information
    Ma, Jiaman
    Chan, Jeffrey
    Ristanoski, Goce
    Rajasegarar, Sutharshan
    Leckie, Christopher
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 105 : 536 - 549