AADT Estimation with Regression Using Centrality and Roadway Characteristic Variables

Accurate traffic volume estimations are critical in nearly every roadway decision, from roadway improvements and maintenance to allocations of funding. While some roadway locations have permanent count stations capable of counting vehicles 24-hours a day throughout the entire year, they are typically only installed at select locations on major roadways (i.e., freeways and major arterials) with high traffic volumes. On lower functional roads and other roadway segments on higher functional class roadways, short term coverage counts are collected and combined with data from permanent count stations to calculate AADT. Short term counts are essential because they provide data from roadways of all functional classes and lane configurations, accounting for varying volumes on all of the roads maintained by an agency. Although necessary, coverage counts can be expensive and can exhaust resources such as manpower, equipment and data analysis. This paper aims to develop an adequate means of estimating AADT on every roadway within a given jurisdiction, while limiting the number of coverage counts needed. The goal is to illustrate a noteworthy time and cost savings in AADT estimation method. The new main deterministic variables introduced are based on the theory of centrality. The root mean square error (RMSE) for the new centrality based AADT model is half compared to RMSE of the travel demand based AADT model. Additionally, it was found that using centrality based AADT estimation model, the number of coverage count stations necessary can be reduced by 65% without compromising the AADT estimation accuracy.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ35 Standing Committee on Highway Traffic Monitoring.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Keehan, McKenzie
  • Dey, Kakan
  • Chowdhury, Mashrur
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Accuracy; Annual average daily traffic; Traffic counts; Traffic estimation; Traffic models; Traffic volume; Travel demand
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01628202
• Record Type: Publication
• Report/Paper Numbers: 17-06649
• Files: TRIS, TRB, ATRI
• Created Date: Mar 7 2017 10:25AM