Turner-Fairbank Highway Research Center

« back to the list

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

Product Type

Journal Article


Amir Mohammadian Amiri, Anurhossein Sadri, Navid Nadimi, and Moe Shams



Full citation

Amiri, A.M., Sadri, A., Nadimi, N., & Shams, M. (2020). A Comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in Predicting the Severity of Fixed Object Crashes among Elderly Drivers. Accident Analysis & Prevention, (138).


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 crashe 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.

Available From

Accident Analysis & Prevention

Link To Journal Article

Link not available.


Crash severity
Elderly drivers
Fixed object crashes
Hybrid models.

« Back to the List

HSIS Summaries

HSIS Summary Reports are two to eight pages in length and include a brief description of the issue addressed, data used, methodology applied, significant results, and practical implications.

Read More

Research Reports

A variety of research studies have been performed using data from HSIS. Many of the final reports prepared are now available electronically.

Read More

Technical Summaries

Research reports are often summarized in executive summaries, technical briefs, or other abbreviated formats. Included here are those road safety summaries that involved research using HSIS data.

Read More

Safety Analysis Tools

In addition to conducting research, HSIS resources are also used to develop products that can be used by practitioners in the analysis of safety problems.

Read More

Other Projects

HSIS data are sometimes used in research studies that result in other types of finished products, such as dissertations, theses, and conference proceedings.

Read More