Abstract
AMHERST, Mass. - Issues such as abrupt changes in speed limits and incomplete lane markings are among the most influential factors that can predict road crashes, finds new research by University of Massachusetts Amherst engineers. The study then used machine learning to predict which roads may be the most dangerous based on these features. Published in the journal Transportation Research Record, the study was a collaboration between UMass Amherst civil and environmental engineers Jimi Oke, assistant professor; Eleni Christofa, associate professor; and Simos Gerasimidis, associate professor; and civil engineers from Egnatia Odos, a publicly owned engineering firm in Greece. The most influential features included road design issues (such as changes in speed limits that are too abrupt or guardrail issues), pavement damage (cracks that stretch across the road and webbed cracking referred to as "alligator" cracking) and incomplete signage and road markings.
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