Machine learning in road accident research: decision trees describing road accidents during cross-flow turns

Article Abstract:

A study using an A1 machine-learning method based on Quinlan's 'ID3' algorithm was conducted to investigate the association of severity and particular causal factors in right-turning accidents and to show that inductively derived decision trees can be effective descriptors of relations among variables in behavioral road accident causation research. Analysis of data provided by the Nottinghamshire Constabulary, UK, showed that severity of accidents involving turning onto a main road can be predicted with 79% of accuracy from the type of the other vehicle, season and junction type.

Decision tree, Decision trees, Machine learning

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Simulation of accident risk displays in motorway driving with traffic

Article Abstract:

The effects of prototype displays designed to convey the accident risk assumed with current driving behavior were studied using a vehicle-based driving simulator. Measures of time headway (TH) and kinetic energy (KE) levels were taken. It was found that the KE display was more effective than the TH display in reducing the proportion of time subjects spent at short headways. The KE display was also effective in reducing the proportion of time subjects spent at high speed.

Risk assessment, Simulation methods, Simulation, Graphic methods

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Technical note: Pattern recognition for road traffic accident severity in Korea

Article Abstract:

An increase in road traffic during the Nineties in Korea has resulted in an increasing number of traffic accidents, causing both safety and economical concerns. Research attempting to characterize the causes of such accidents and identify severity related factors by using traffic accident records is presented.

Traffic, Traffic safety, Korea

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