A Statistical Model of Regional Traffic Congestion in the United States

Roadway congestion has been a major public policy issue in the United States for many years. There has been an ongoing debate as to whether congestion can be reduced significantly through adding additional roadway capacity, or if adding capacity would only induce traffic growth without any long-term reduction in congestion. The availability of real-time traffic data supports cross-sectional analysis across regions to study factors underlying congestion. In a regression analysis of 74 regions, it is found that more arterial capacity is strongly related to less congestion, but that more freeway capacity is not. The public policy implications are that it is critical that an adequate network of streets be constructed in growing areas rather than relying too much on a system of freeways. In already-congested areas, arterial capacity improvements likely would be more effective at reducing congestion than adding freeway capacity. Otherwise, the regression model suggests that congestion is more a sign of regional success than a problem than can be solved. Only two other independent variables were found to be highly significant in predicting congestion. Higher incomes increase congestion. Higher incomes attract population growth, which also increases congestion.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADA20 Standing Committee on Metropolitan Policy, Planning, and Processes.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Marshall, Norman L
  • Conference:
  • Date: 2016


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p
  • Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01590119
  • Record Type: Publication
  • Report/Paper Numbers: 16-0475
  • Files: PRP, TRIS, TRB, ATRI
  • Created Date: Feb 11 2016 3:38PM