Leveraging high-resolution traffic data to understand the impacts of congestion on safety
Since vehicle crashes in urban area may potentially cause higher societal costs than those in rural area, it is critical to understand the contributing factors of urban crashes, especially congestions. This paper analyzes the impacts of segment characteristics, traffic-related information and weather information on monthly crash frequency based on a case study in Iowa, U.S. Random parameter negative binomial (RPNB) model was employed. Considering that same factor may impact crash frequency differently on segments with different congestion level, the heterogeneity in random parameter means was introduced and discreetly examined. Data from 77 directional segments and 24 months (2013-2014) were used in this study. The empirical results show that segment length and maximum snow depth have fixed impacts while number of lanes, shoulder width and trailers percentage have random impacts on crash frequency. In addition, heterogeneous behaviors of the random factors were identified between segments with different congestion level. For example, the model results indicate that the increase of left shoulder width tends to decrease crash frequency more under congested condition than under uncongested condition.
This article is published as Huang, Tingting, Shuo Wang, and Anuj Sharma. "Leveraging high-resolution traffic data to understand the impacts of congestion on safety." In 17th International Conference Road Safety On Five Continents (RS5C 2016), Rio de Janeiro, Brazil, 17-19 May 2016. Statens väg-och transportforskningsinstitut, 2016. Posted with permission.