State-wide Traffic Volume Estimation for Non-freeway Roads Using Probe-vehicle Data and Machine Learning Methods

Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. High quality traffic volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume data on freeways is typically collected through continuous count stations (CCS), while there is low traffic volume observability on non-freeway roads. Addressing this issue, this study uses a state-of-the-art machine learning method, namely XGBoost, to estimate state-wide traffic volumes on non-freeway roads using probe-vehicle data. North Carolina was chosen for this case study due to its prevalence of CCSs on non-freeway roads. The results show that the proposed algorithm can estimate hourly volumes with mean absolute error (MAE) of 57 vehs/hr and R-squared of 0.87. The method also captures the abnormal traffic patterns during hurricane Florence.

• Record URL:
  https://annualmeeting.mytrb.org/OnlineProgram/Details/15715
• Supplemental Notes:
  • This paper was sponsored by TRB committee AED50 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Hou, Yi
  • Garikapati, Venu
  • Hoehne, Christopher
  • Kasundra, Kevin
  • Young, Stanley
• Conference:
  • Transportation Research Board 100th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2021-1-5 to 2021-1-29
• Date: 2021

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 16p

Subject/Index Terms

• TRT Terms: Estimating; Machine learning; Probe vehicles; Roads; States; Traffic data; Traffic volume
• Geographic Terms: North Carolina
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01764067
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-00393
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 10:57AM