Published In

Transportation Research Record

Document Type

Post-Print

Publication Date

9-1-2018

Subjects

Traffic accidents -- Oregon, Roads -- Oregon -- Safety measures, Pedestrians -- Oregon -- Safety measures, Cyclists -- Oregon -- Safety measures

Abstract

Methods for identifying and prioritizing high-crash locations for safety improvements are generally crash-based. There are fewer reported crashes involving non-motorized users and, in most states, reported crashes must involve a motor vehicle. This means that minor, non-injury events are not reported and those crashes that are reported tend to be more severe. Selecting projects based only on crash performance is sometimes limiting for these crash types and predicting where these crashes will occur next is also a challenging task. An alternative to crash-based selection is to develop risk-based criteria and methods. This paper presents the results of a research effort to develop a risk-scoring method with weights derived from data for use in project screening and selection in Oregon. To develop the risk model, data were collected from 188 segments and 184 intersections randomly selected on both state and non-state roadways. Geometric, land use, volume, and crash data were collected from Google Earth, EPA’s Smart Location Database, and the Oregon Department of Transportation crash database from 2009 to 2013. The sample included 213 bicycle and pedestrian crashes on the segments and 238 at intersections. Logistic regression models were developed and the outputs used to create pedestrian and bicycle risk-scoring tools for segments and intersections. The risk-scoring tool was applied to safety projects identified in the 2015 All Roads Transportation Safety (ARTS) project lists from Oregon. The risk scores for the case study applications aligned reasonably well with the project’s benefits-costs estimates.

Methods for identifying and prioritizing high-crash locations for safety improvements are generally based on reported crashes (1). There are fewer reported crashes involving non-motorized users and, in most states, reported crashes must involve a motor vehicle. This means that minor, non-injury events are not reported and those crashes that are reported tend to be more severe. This results in fewer crashes for network screening techniques to identify locations. Further, since there is clear evidence that the decision to make non-motorized trips by bicycling or walking is influenced by the perception of safety (2), locations that have deficient geometric or operational features may not be identified by crash-based screening methods. Exposure data for non-motorized users is also challenging to obtain at the network level (3). Selecting projects based only on crash performance is sometimes limiting for these crash types and predicting where these crashes will occur next is also a challenging task. An alternative to crash-based selection is to develop risk-based criteria and methods.

The Oregon Department of Transportation (ODOT) has identified pedestrian and bicycle crashes as a primary focus area for investing in infrastructure funding. In 2015, 17.7% of the 445 traffic fatalities in Oregon were non-motorized (4). ODOT has appropriated approximately $4 million annually in the ARTS program to address this issue. Prior to this research project, ODOT conducted a systemic safety analysis of pedestrian and bicycle safety (5). As part of the work, a crash frequency-based and risk-based prioritization methodology were developed. The quantification of risk factors and the magnitude of their influence was constrained by the additional data that the project was able to collect, and many of the risk scores were based on engineering judgment.

The objective of the research described in this paper was to develop a risk-scoring method with weights derived from data. The intent is for the risk method to be used in project screening and selection in Oregon. Following a brief background and literature summary, the data collection methodology on segments and at intersections is described. In the methodology, the logistic modeling approach and conversion of the outputs to a risk-scoring tool is described. The model results and final tool are then described. Finally, the application of the risk-scoring tool to several projects recommended for funding in the ARTS project list is presented.

Description

Copyright National Academy of Sciences: Transportation Research Board 2018

DOI

10.1177/0361198118794285

Persistent Identifier

https://archives.pdx.edu/ds/psu/28923

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