Welcome to ROSA P |
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Developing inexpensive crash countermeasures for Louisiana local roads.
  • Published Date:
    2014-03-01
  • Language:
    English
Filetype[PDF-2.88 MB]


Details:
  • Publication/ Report Number:
  • Resource Type:
  • Geographical Coverage:
  • 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.
  • Format:
  • Main Document Checksum:
  • Supporting Files:
    No Additional Files
No Related Documents.
You May Also Like: