A Comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in Predicting the Severity of Fixed Object Crashes among Elderly Drivers

Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury and fatal crashes. A substantial portion of ROR fatalities occur in collisions with fixed objects at the roadside. Thus, this study seeks to investigate the severity of ROR crashes where elderly drivers, aged 65 years or more, hit a fixed object. The reason why the present study investigates this issue among older drivers is that, comparing to younger drivers, this age group of drivers have different psychological and physical features. Because of these differences, they are more likely to get injured in ROR types of crashes. This paper applies two types of Artificial Intelligence (AI) techniques, including hybrid Intelligent Genetic Algorithm and Artificial Neural Network (ANN) using the crash information of California in 2012 obtained from Highway Safety Information System (HSIS) database. Although the results showed that the developed ANN outperformed the hybrid Intelligent Genetic Algorithm, the hybrid approach was more capable of predicting high-severity crashes. This is rooted in the way the hybrid model was trained by taking advantage of the Genetic Algorithm (GA). The results also indicated that the light condition has been the most significant parameter in evaluating the level of severity associated with fixed object crashes among elderly drivers, which is followed by the existence of the right and left shoulders. Following these three contributing factors, cause of collision, Average Annual Daily Traffic (AADT), number of involved vehicles, age, road surface condition, and gender have been identified as the most important variables in the developed ANN, respectively. This helps to identify gaps and improve public safety towards improving the overall highway safety situation of older drivers.

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  • English

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  • Accession Number: 01734637
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 24 2020 10:51AM