Modeling traffic barriers crash severity by considering the effect of traffic barrier dimensions

Traffic barriers are in widespread all around the USA as safety countermeasures for reducing the severity of run-off-road crashes. The effect of traffic barriers’ dimension had been ignored in past real-world crash studies due to the considerable cost and time needed for collecting field data. This paper presented two new analytical models to investigate the effect of different variables on the severity of crashes involving traffic barriers, and end treatments. For this reason, a field survey was conducted on over 1.3 million linear feet of traffic barriers (approximately 4,176 miles road) in Wyoming to measure traffic barriers’ geometric features like height, length, offset, and slope rate. The collected data included 55% of all non-interstate roads of Wyoming. Based on results, the crashes involving box beam barriers were less severe than the crashes involved with W-beam or concrete barriers. The traffic barriers with a height between 28 and 31 in. were found safer than the traffic barriers shorter than 28 in., while there was no significant difference between the traffic barriers taller than 31 in. to those shorter than 28 in. in terms of crash severity. The end treatments located nearer to the traffic lane had lower crash severity.

Application of multinomial and ordinal logistic regressionto model injury severity of truck crashes, using violationand crash data

In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

An investigation of influential factors of downgrade truck crashes:A logistic regression approach

Truck crash occurrence causes extensive damage to lives and property. Truck crashes on downgrades exacerbate these costs due to the likelihood of a runaway being involved.Highway agencies have continuously sought engineering measures to reduce the incidence of such crashes. However, most past studies on truck crashes have focused on level roadway sections of highways without considering the effects of downgrades. The difference in geometric characteristics of downgrades and the mechanics of truck operations on such sections mean different factors may be at play in contrast to level roadway sections.This paper investigated the factors influencing truck crashes on downgrades; an attempt to fill in some of the research gaps. An empirical analysis of factors affecting truck crashes on two-lane downgrade roadways in Wyoming was carried out using a binary logistic regression technique. After calibrating the model, the effect of each significant variable was determined using theoretical concepts established in previous studies and engineering intuition. Crash factors including driver gender and age, weather, lighting and road conditions, number of crest curves, crash type, number of driveways, day of week and posted speed limit were found to be significant. The results of the study offer new understandings into how the identified factors influence truck crashes on downgrades.

Investigating the relationship between crash severity,traffic barrier type,and vehicle type in crashes involving traffic barrier

Roadside safety is one of the important components of highway systems due to its considerable rate of high-severity crashes.Traffic barriers play a key role in reducing the crash severity and saving more lives on roadsides but choosing an inappropriate traffic barrier could decrease traffic barriers’efficiency.This paper investigated the variables affecting crash severity in different traffic barrier types(cable,guardrail,and rigid)and vehicle types(truck,and non-truck).For this purpose,an ordinal logistic regression approach was conducted on the crash data collected between 2007 and 2016 in Wyoming.According to the results,different traffic barrier types show different relationships to crash severity based on posted speed limit at the site.In guardrail segments,a high-severity crash is more likely when the posted speed limit is more than 55 mph;however,cable barrier crashes were less severe in high-speed limit(>55 mph)areas.While light vehicles were more vulnerable to crashes hitting rigid barriers,no significant difference was found between performances of cable and rigid barriers in terms of truck crash severity.The analytical models proposed in this study would make a clear view for designers and decision-makers in selecting the most appropriate traffic barrier.Also,the effective factors introduced in each model could be used to rank the risk posed by existing traffic barrier segments based on traffic barrier type,truck volume percentage,and other highway characteristics.

Partial proportional odds model for analyzing pedestrian crashes,threshold heterogeneity by scale and proportional odds factor

Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted that drivers under influence,type of vehicle,location of crashes,estimated speed of vehicles,driving over the recommended speed are some of factors contributing to the severity of crashes.In this study,we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values.However,it has been argued that this assumption might be unrealistic,especially at the presence of extreme values.Thus,the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes,or proportional odds effects.In addition,we accounted for the spread rate,or scale,of the model’s latent distribution of pedestrian crashes.The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model.Comparisons were also made across standard normal,simple partial ordinal model,and partial ordinal accounting for scale heterogeneity.In addition,various potential threshold structures such as symmetric and flexible were considered,but similar goodness of fits were observed across all those models.Extensive discussion has been made regarding the formulation of the implemented methodology,and its implications.