Application of Vehicle Probe Data in Estimating Traffic Volumes: a Maryland Case Study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the authors examine applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and propose a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ35 Standing Committee on Highway Traffic Monitoring.
• Authors:
  • Sekula, Przemyslaw
  • Marković, Nikola
  • Vander Laan, Zachary
  • Farokhi Sadabadi, Kaveh
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

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

Subject/Index Terms

• TRT Terms: Case studies; Data analysis; Probe vehicles; Traffic estimation; Traffic volume
• Geographic Terms: Maryland
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01663663
• Record Type: Publication
• Report/Paper Numbers: 18-05582
• Files: TRIS, TRB, ATRI
• Created Date: Mar 22 2018 11:57AM