Paper Details

TRB 91st Annual Meeting (January 22-26, 2012)

Event Number:	648
Event Title:	Highway Safety Performance
Event Date:	Jan 24 2012 7:30PM- 9:30PM
Event Location:	Marriott, Salon 2
Event Description:
Event Agenda:

Comparing Highway Safety Manual Predictive Method to Traditional Ranking Methods: Case Study of Intersections in Corvallis, Oregon (12-4070)

An intersection, due to its vehicle and pedestrian conflict movements, experiences complex traffic situations that might contribute to crashes. It is important to develop a transportation safety planning process that correctly identifies hazardous locations so that an agency can implement improvements that provide the roadway users a safe transportation system. The predictive method in the Highway Safety Manual (HSM) provides quantitatively reliable, science-based estimates for urban and suburban intersection and segment locations (AASHTO, 2010). This method incorporates traffic volume, site geometric characteristics, and traffic control features to approximate predicted crashes. The procedure then applies the Empirical Bayes (EB) method to combine the predicted crash frequency with historic crash data. The primary objective of this study is to use the predictive method in the HSM to identify high crash locations that have potential for reducing the number and severity of crashes and to contrast three traditional network screening safety procedures (the crash frequency method, the crash rate method, and the crash severity method) to results obtained using the HSM predictive method. The authors used 24 intersections in the Corvallis, Oregon urban area as a case study. The results demonstrated that developing a ranked list of candidate intersection using these four methods identified different locations since each method specifically evaluates a particular safety metric. The authors further present how the use of the HSM predictive procedure, in combination with other ranking methods, can help an agency confidently establish funding initiative objectives and identify target sites that will directly meet these goals.

Authors
     Zhou, Yanfen , Oregon State University
     Dixon, Karen K., Texas A&M Transportation Institute