Abstract
The purpose of this study is to investigate and compare the significant influencing factors of driver injury severity in single-vehicle (SV) crashes under foggy and clear weather conditions. Based on data for SV crashes in Shandong Province, China, the mixed logit model (MLM) was employed to interpret driver injury severity for SV crashes in clear and foggy weather. The results showed that there are significant differences in the influencing factors of the severity of SV crashes in foggy and clear weather. Specifically, 15 factors are significantly associated with the severity of SV crashes in clear weather, and 18 factors are significantly associated with the severity of SV crashes in foggy weather. In addition, young drivers (age < 30), non-dry road surfaces, and signal control significantly influence the severity of foggy weather crashes but not clear weather crashes. Self-employment and weekends have significant effects on the severity of crashes only in clear weather. Interestingly, drivers whose occupation is farming showed opposite trends in the effect of crash severity in foggy and clear weather. Based on the findings of this research, some potential countermeasures can be adopted to reduce crash severity in foggy and clear weather.
Highlights
Some differences in SV crashes between foggy and clear weather may exist [4, 5]. e low visibility caused by fog affects driver behavior and the driving environment, which can lead to contributing effects on traffic collisions that are different from those in clear weather
MNL involves two limitations: (1) it is based on the assumption of independence of irrelevant alternatives (IIA); this hypothesis is not always accepted as the model can be influenced by unobserved factors [17]; and, (2) the parameter is treated as a fixed value, which cannot reflect the individual heterogeneity across observations; this may lead to bias in parameter estimation
Gender has a significant influence on driver injury severity of both SV crashes in foggy weather (SVCF) and SV crashes in clear weather (SVCC) crashes. e marginal effect shows that compared with female drivers, the probability of serious or fatality injury (SFI) injury of male drivers in SVCF and SVCC accidents decreased by 4.5% and 0.3%, respectively [24, 25]
Summary
Research, J 3). e utility function of the ith crash with the injury severity j can be specified as the following equation: Uij xi′j βj + εij, (i 1, . . . , n; j 1, . . . , J), (1). E utility function of the ith crash with the injury severity j can be specified as the following equation: Uij xi′j βj + εij, Where Uij is the utility when the severity of the ith crash is j, xi′j represents the independent variables, εij is the observed disturbance term, βj is the coefficient of the independent variable, and n indicates sample size. Assuming that εij obeys the generalized extreme value distribution, the probability of injury severity j for the ith crash can be expressed as equation (2), and it is known as the MNL model: Pyi. Where f(βj | φ) represents the probability density function of parameter βj, φ represents an unknown characteristic parameter of the probability density function, such as the mean value μ and the variance σ of normal distribution, which can be expressed as φ μ, σ, and parameter βj varies across observations which may be random or fixed. Other characteristics Traffic controls No control∗ Signal control Stop-yield sign Other control methods Week Monday or Friday Tuesday– ursday∗ Weekend Intersection No∗ Yes Time of accident 00 : 00–07 : 00 07 : 00–09 : 00
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