Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data

Abstract

This study investigated the effect of the interaction between roadway geometric features and real-time weather and traffic data on the occurrence of crashes on a mountainous freeway. The Bayesian logistic regression technique was used to link a total of 301 crash occurrences on I-70 in Colorado with the space mean speed collected in real time from an automatic vehicle identification (AVI) system and real-time weather and roadway geometry data. The results suggested that the inclusion of roadway geometrics and real-time weather with data from an AV I system in the context of active traffic management systems was essential, in particular with roadway sections characterized by mountainous terrain and adverse weather. The modeling results showed that the geometric factors were significant in the dry and the snowy seasons and that the likelihood of a crash could double during the snowy season because of the interaction between the pavement condition and steep grades. The 6-min average speed at the crash segment during the 6 to 12 min before the crash and the visibility 1 h before the crash were found to be significant during the dry season, whereas the logarithms of the coefficient of variation in speed at the crash segment during the 6 to 12 min before the crash, the visibility 1 h before the crash, as well as the precipitation 10 min before the crash were found to be significant during the snowy season. The results from the two models suggest that different active traffic management strategies should be in place during these two distinct seasons.

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

“…Ahmed et al (2012) used a Bayesian logistic regression technique to assess crash occurrence with freeway geometry and real time weather and traffic data. They found that crash likelihood could double between the dry and snow seasons, because of the interaction between pavement conditions and steep grades.…”

“…For instance, several researchers have assessed the effect of weather elements, i.e., snow fall (Andrey, 2010;Eisenberg and Warner, 2005;Hermans et al, 2006;Khattak and Knapp, 2001), rain fall (Abdel-Aty and Pemmanaboina, 2006;Usman et al, 2012;, wind speed (Jung et al, 2011;Usman et al, 2012), temperature (Bergel et al, 2013;Brijs et al, 2008;El-Basyouny and Kwon, 2012 crash occurrence (Ahmed et al, 2012;Brijs et al, 2008;Usman et al, 2012;, whereas other researchers investigated these effects on crash severity (Andrey, 2010;Bergel et al, 2013;Eisenberg, 2004;El-Basyouny and Kwon, 2012). While the above studies are useful in understanding the effect of adverse weather, it is not always possible to identify suitable countermeasures based on the results of these studies.…”

Approach-level real-time crash risk analysis for signalized intersections

“…With the rapid development of traffic surveillance system and detection technologies, real-time traffic data are not only available on freeways and expressways but also on urban arterials (including road segments and intersections). During the past decade, an increasing number of studies have investigated the crash likelihood on freeways by using real-time traffic and weather data (Oh et al 2001, Lee et al 2003, Abdel-Aty et al 2004, Zheng et al 2010, Ahmed et al 2012a, Xu et al 2013a, Xu et al 2013b, Basso et al 2018, Theofilatos et al 2018. It is worth noting that Theofilatos et al (2018) investigated crash occurrence by utilizing real-time traffic data while considering that the number of crashes is very few, and they could be considered as rare events.…”

Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data

“…In literature, speed variations have been linked to high risk of accidents on freeways (Zheng et al, 2010;Xu et al, 2013b;Ahmed et al, 2012b;Hassan and Abdel-Aty, 2013). This finding of the Bayesian model suggests that large variations in speed have an influence on PTW accidents in urban roads as well.…”

“…It is also noted, that the vast majority of studies exploit data from freeways. Concerning accident likelihood in particular, previous research on this topic suggests that the common risk factors are mainly the variations in traffic conditions (Ahmed et al, 2012a and2012b;Ahmed and Abdel-Aty 2012;Xu et al, 2013a andZheng et al, 2010) and low visibility or adverse weather conditions (Xu et al, 2013a;Ahmed et al, 2012b;Abdel-Aty et al, 2012).…”