Explaining crash modification factors: Why it's needed and how it might be done.

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ID: 3418
2019
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Abstract
Although the Highway Safety Manual (HSM) now provides empirical tools for predicting the safety consequences of highway engineering decisions, these tools represent the driver and vehicle conditions prevailing in the United States during the last few decades. As automated vehicles improve in capability and increase in market share these conditions will change, possibly reducing the accuracy of HSM predictions. Assessing the transferability of a crash modification factor to new situations almost certainly requires an explanation of how the modification achieves its effect, but at present there is little guidance on how such explanations might be posed and tested. This paper describes the use of micro-simulation to develop an explanation of how pedestrian hybrid beacons (PHB) modify pedestrian crash likelihood. Since the literature indicated that PHBs can affect both pedestrian and driver behavior it was necessary to include both possibilities in the model. To simulate injury severity distributions similar to those recorded in a crash database it was necessary to propose that almost all simulated drivers attempt to brake in pedestrian/vehicle encounters. Then changing the simulated fraction of careful pedestrians from between 0% and 30% to between 80% and 90% gave simulated crash modification factors similar to estimates reported in the literature. The resulting working hypothesis then is that PHBs achieve their crash reduction effect in large part by modifying pedestrian behavior. This is not so much a direct observation as it is an inference to the best explanation. That is, the support for the hypothesis comes from its ability to explain the data at hand. This hypothesis should be tested further, and additional tests are proposed.
Reference Key
davis2019explainingaccident Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Davis, Gary A;
Journal accident; analysis and prevention
Year 2019
DOI
S0001-4575(19)30445-2
URL
Keywords Keywords not found

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