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Developing inexpensive crash countermeasures for Louisiana local roads.

Filetype[PDF-2.88 MB]


English
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Details:

  • Creators:
  • Corporate Creators:
    Louisiana State University (Baton Rouge, La.)
  • Corporate Contributors:
    United States. Federal Highway Administration
  • Subject/TRT Terms:
  • Publication/ Report Number:
    FHWA/LA.13/503
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    Louisiana
  • Corporate Publisher:
    Louisiana. Dept. of Transportation and Development
  • Abstract:
    Although 40% of all crashes in Louisiana are on local roads, local road safety improvement programs have not received

    the attention needed to reduce crashes. Local road crash countermeasures are an important part of the overall efforts to

    reduce crashes and their severity in Louisiana. The efforts to develop a local road safety program are hampered by the lack

    of appropriate risk assessment that enables local agencies to reduce crashes using low cost countermeasures. This paper

    provides a methodology that can be used by local agencies to deploy countermeasures based on a risk assessment and

    optimization to meet a fixed budget. First, a statistical model is presented to assess the risk of local road segments taking

    into account AADT and geometric features of the road segment or intersection. Secondly, low cost countermeasures are

    recommended for individual road segments and costs for improvements are assessed. Thirdly, a score which allows the

    ranking of road projects is developed for each road segment. This score incorporates the risk associated with the observed

    number of crashes, the benefits of improvements, and the total cost of a project. Finally, guidelines for a local road safety

    improvement program are presented to allow local agencies to institute procedures for a systematic system-wide road

    improvement methodology. The deliverables include an Excel application that uses OLAP to obtain a ranking of

    candidates for road improvements. This application makes use of crash data, engineering features, and AADT to compute

    empirical Bayes (EB) estimates and tail probabilities for each road segment and intersection. Road segments and

    intersections with a tail probability below 5% are selected as candidates for countermeasures. These candidates are

    evaluated using Google Earth, countermeasures are suggested, and costs and benefits of the countermeasures are obtained

    using published information. The resulting road improvement projects are then ranked using multi criteria DEA including

    costs, benefits and crash risks.

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