Capturing Car-Following Behaviors by Deep Learning

In this paper, the authors propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, they assume that drivers’ actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which the authors reduce human interference to the minimum degree. Specially, they use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides them more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.

• Record URL:
  https://doi.org/10.1109/TITS.2017.2706963
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Wang, Xiao
  • 0000-0002-0008-0659
  • Jiang, Rui
  • Li, Li
  • 0000-0002-7580-3559
  • Lin, Yilun
  • Zheng, Xinhu
  • Wang, Fei-Yue
• Publication Date: 2018-3

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 910-920
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 3
  • 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: Behavior; Car following; Machine learning; Microscopic traffic flow; Neural networks; Traffic data
• Uncontrolled Terms: Deep learning
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01664594
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Mar 28 2018 10:53AM