Effect of time intervals on k-nearest neighbors model for short-term traffic flow prediction

The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, the authors extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval.

• Record URL:
  • https://doi.org/10.7307/ptt.v31i2.2811
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03535320
• Supplemental Notes:
  • © 2019 Zhao Liu et al.
• Authors:
  • Liu, Zhao
  • Qin, Xiao
  • Huang, Wei
  • Zhu, Xuanbing
  • Wei, Yun
  • Cao, Jinde
  • Guo, Jianhua
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Pagination: pp 129-139
• Serial:
  • PROMET-Traffic & Transportation
  • Volume: 31
  • Issue Number: 2
  • Publisher: University of Zagreb
  • ISSN: 0353-5320
  • EISSN: 1848-4069
  • Serial URL: https://traffic2.fpz.hr/index.php/PROMTT
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Mathematical prediction; Time intervals; Traffic flow; Traffic forecasting; Traffic models
• Uncontrolled Terms: Autoregressive integrated moving average models; K-nearest neighbor algorithm
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01712433
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 24 2019 10:36PM