Long-Term Traffic Speed Prediction Based on Multiscale Spatio-Temporal Feature Learning Network

Speed plays a significant role in evaluating the evolution of traffic status, and predicting speed is one of the fundamental tasks for the intelligent transportation system. There exists a large number of works on speed forecast; however, the problem of long-term prediction for the next day is still not well addressed. In this paper, the authors propose a multiscale spatio-temporal feature learning network (MSTFLN) as the model to handle the challenging task of long-term traffic speed prediction for elevated highways. Raw traffic speed data collected from loop detectors every 5 min are transformed into spatial–temporal matrices; each matrix represents the one-day speed information, rows of the matrix indicate the numbers of loop detectors, and time intervals are denoted by columns. To predict the traffic speed of a certain day, nine speed matrices of three historical days with three different time scales are served as the input of MSTFLN. The proposed MSTFLN model consists of convolutional long short-term memories and convolutional neural networks. Experiments are evaluated using the data of three main elevated highways in Shanghai, China. The presented results demonstrate that they approach outperforms the state-of-the-art work and it can effectively predict the long-term speed information.

• Record URL:
  https://doi.org/10.1109/TITS.2018.2878068
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Zang, Di
  • Ling, Jiawei
  • Wei, Zhihua
  • Tang, Keshuang
  • Cheng, Jiujun
• Publication Date: 2019-10

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3700-3709
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 20
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Intelligent transportation systems; Machine learning; Mathematical prediction; Matrices (Mathematics); Neural networks; Spatial analysis; Time series; Traffic data; Traffic speed
• Geographic Terms: Shanghai (China)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01721365
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Oct 31 2019 5:02PM