We study the severity of accidents on the German Autobahn in the state of North Rhine-Westphalia using data for the years 2009 until 2011. We use a multinomial logit model to identify statistically relevant factors explaining the severity of the most severe injury, which is classified into the four classes fatal, severe injury, light injury and property damage. Furthermore, to account for unobserved heterogeneity we use a random parameter model. We study the effect of a number of factors including traffic information, road conditions, type of accidents, speed limits, presence of intelligent traffic control systems, age and gender of the driver and location of the accident. Our findings are in line with studies in different settings and indicate that accidents during daylight and at interchanges or construction sites are less severe in general. Accidents caused by the collision with roadside objects, involving pedestrians and motorcycles, or caused by bad sight conditions tend to be more severe. We discuss the measures of the 2011 German traffic safety programm in the light of our results.
"In the area of accident severity research, continuous efforts have been conducted in order to investigate the relationship between the level of severity (dependent variable) and a set of explanatory variables, which usually include: driver attributes (e.g., age and gender), vehicle features (e.g., body type, vehicle age and number of vehicles involved in the accident), road characteristics (e.g., number of lanes, road surface conditions, intersection control and types of road), and accident characteristics (e.g., accident's main cause). Occasionally, the influence of other variables on accident severity like speed limit, day of the week, time of the day, average traffic characteristics (AADT), weather and traffic conditions have also been scrutinized (Delen et al., 2006; Manner and Ziegler, 2013; Torrão, 2013). From previous studies, it is worth mentioning a recent study carried out by Christoforou et al. (2010), mainly because it offers a comprehensive literature review on the subject. "
[Show abstract][Hide abstract] ABSTRACT: The ordered probit model is used to examine the contribution of several factors to the injury severity faced by motor-vehicle occupants involved in road accidents. The estimated results suggest that motor-vehicle occupants travelling in light-vehicles, at two-way roads, and on dry road surfaces tend to suffer more severe injuries than those who travel in heavy-vehicles, at one-way roads, and on wet road surfaces. Additionally, the driver's seat is clearly the safest seating position, urban areas seem to originate less serious accidents than rural areas, and women tend to be more likely to suffer serious or fatal injuries than men.
"Both ordered and unordered models have their own unique benefits and limitations, and the choice of one method over the other is governed by the availability and characteristics of the data and involves taking tradeoffs into consideration (Savolainen et al., 2011; Ye and Lord, 2014). The mixed logit method has been widely applied to analyze crash severities (Anastasopoulos and Mannering, 2011; Chen and Chen, 2011; Haleem and Gan, 2013; Islam et al., 2014; Kim et al., 2008, 2013; Malyshkina and Mannering, 2008; Manner and Wünsch-Ziegler, 2013; Milton et al., 2008; Moore et al., 2011; Morgan and Mannering, 2011; Shaheed et al., 2013; Ye and Lord, 2014; Weiss et al., 2014). The benefit of using this method is that it can accommodate individual unobserved heterogeneity by allowing parameters to differ across observations, and thus, can provide more reliable parameter estimates. "
[Show abstract][Hide abstract] ABSTRACT: While there have been many studies analyzing crash severity, few studies have accounted for unobserved heterogeneity and compared different crash severity models. The objective of this paper is to investigate the differences between two preferred methods for accommodating individual unobserved heterogeneity, the mixed logit and latent class methods, in exploring the relationship between heavy truck crash severity and its contributing factors. To achieve this, a large sample of crash data on multiple vehicle crashes involving a heavy truck on public roadways in Iowa from 2007 to 2012 was collected. The comparison of the two methods lied on model fit, inferences, and predicted crash severity outcome probabilities. The results suggested a slight superiority of the latent class method in terms of model fit; however, the mixed logit predicted probabilities for all three levels of injury severities were closer (on average) to the observations than the ones predicted by the latent class model. Only a few notable differences in the inferences were found between the two models.
"Therefore, the two indicators should be considered together and combined in one model system. Concerning severity analysis, which includes mainly three aspects, that is, number of fatalities, number of injuries, and property damage, most of the existing researchers investigated it as one comprehensive indicator; for example, Mannera and Wünsch-Ziegler took accident severity as one independent variable with four alternatives, namely, fatal, severe injury, light injury, and property damage. Milton et al.  defined severity levels as property damage only, possible injury, and injury. "
[Show abstract][Hide abstract] ABSTRACT: This paper presents a model system to predict severity and duration of traffic accidents by employing Ordered Probit model and Hazard model, respectively. The models are estimated using traffic accident data collected in Jilin province, China, in 2010. With the developed models, three severity indicators, namely, number of fatalities, number of injuries, and property damage, as well as accident duration, are predicted, and the important influences of related variables are identified. The results indicate that the goodness-of-fit of Ordered Probit model is higher than that of SVC model in severity modeling. In addition, accident severity is proven to be an important determinant of duration; that is, more fatalities and injuries in the accident lead to longer duration. Study results can be applied to predictions of accident severity and duration, which are two essential steps in accident management process. By recognizing those key influences, this study also provides suggestive results for government to take effective measures to reduce accident impacts and improve traffic safety.
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