Journal Article
Amirreza Nickkar, Ali Yazdizadeh, and Young-Jae Lee
Nov-20
Nickkar, A., Yazdizadeh, A., Lee, Y.J. (2020). Investigating Factors That Contribute to Freeway Crash Severity Using Machine Learning. Advances in Transportation Studies, (52), pp 131-142.
While the cost of crashes nears $1 trillion a year in the U.S., the availability of high-resolution Highway Safety Information System (HSIS) data allows researchers to conduct an in-depth analysis of factors that contribute to crashes, and design appropriate interventions. The current study has two main goals: First, finding possible relationships between contributing factors and the severity of crashes on urban expressways and freeways, and, second, improving the prediction accuracy by using a machine learning approach to classify the crash severity and evaluate the performance of this classifier algorithm. The authors used the crash data on urban expressways and freeways from 2005 to 2015 in the state of Washington provided by HSIS. This study uses the random forest model to predict the severity of crashes based on the attributes. The random forest model was able to predict the severity of crashes with 88.6 % accuracy while the authors observed precision of 89.9%, 62.1%, and 40.1% for classes 1-3, respectively. The authors found that crash type, functional class of road, AADT, and location type played a more important role than other variables in predicting the severity of crashes. Furthermore, lighting conditions, weather, year of the crash, and the road characteristics did not have much effect on the severity of crashes.
Advances in Transportation Studies
Link not available.
Data mining
Machine learning
Crash severity
Crash analysis
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.
A variety of research studies have been performed using data from HSIS. Many of the final reports prepared are now available electronically.
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.
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.
HSIS data are sometimes used in research studies that result in other types of finished products, such as dissertations, theses, and conference proceedings.
