Details:

Creators:
Schneider, Helmut ; Sun, Xiaoduan ; Kelle, Peter
Schneider, Helmut ; Sun, Xiaoduan ; Kelle, Peter Less -
Corporate Creators:
Louisiana State University (Baton Rouge, La.)
Corporate Contributors:
United States. Federal Highway Administration
Subject/TRT Terms:
[+]
Annual Average Daily Traffic Bayes' Theorem Benefit Cost Analysis Countermeasures Highway Safety Methodology Risk Assessment Safety Programs Traffic Accidents
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.


Format:
PDF
Funding:
10-5SS ; 30000240
10-5SS ; 30000240 Less -
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:59657a8524701151911a3c4c4aed8569dc4437c3f228675b02d6dde6b9bd646d
Download URL:
https://rosap.ntl.bts.gov/view/dot/27172/dot_27172_DS1.pdf
File Type:

Supporting Files

• No Additional Files

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