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Developing countries are primarily associated with poor roadway and lighting infrastructure challenges, which has a considerable effect on their traffic accident fatality rates. These rates are further increased as bus/minibus drivers indulge in risky driving, mainly during weekends when traffic and police surveillance is low to maximise profits. Although these factors have been mentioned in the literature as key indicators influencing accident severity of buses/minibuses, there is currently no study that explored the complex mechanisms underpinning the simultaneous effect of pavement and light conditions on the generation of accident severity outcomes while considering weekly temporal stability of the accident-risk factors. This study seeks to investigate the variations in the effect of contributing factors on the severity of bus/minibus accidents in Ghana across various combinations of pavement and light conditions and to identify the exact effects of weekdays and weekends on severity outcomes using a random parameter ordered logit model with heterogeneity in the means to account for unobserved heterogeneity in the police-reported data. Preliminary analysis demonstrated that accident-risk factors used in the models were temporally unstable, warranting the division of the data into both weekend and weekday time-periods. A wide variety of factors such as sideswipes, median presence, merging, and overtaking had significantly varying effects on bus/minibus accident severities under different combinations of pavement and light conditions for both weekdays and weekends. Insights drawn from this study, together with the policy recommendations provided, can be employed by engineers and policymakers to improve traffic safety in developing nations., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge. Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn't exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction. Future research could consider the influences that temporal instability of contributing features has on the model performances., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

In fatal road vehicle accidents motorcycles are overrepresented per vehicle kilometre travelled. Fatal accidents involving motorcycles contain mode specific characteristics, and in common with fatal accidents involving all road users, speed typically presents as a significant contributory factor. The aim of the present study is to provide quantitative estimates for the contribution of speed in situations commencing from the reaction location to the safety critical event involving a motorcyclist and resulting in a fatal accident. The contribution of speed to the resulting accident risk and accident severity is considered from this reaction point. A speed-squared versus stopping distance domain, termed the severity-risk space, is examined to determine the accident measures. The defined accident measures, namely, accident risk, accident severity and accident severity risk are calculated for sixteen fatal accidents from a police dataset of recent UK motorcycle accidents. The estimates of the defined measures are provided in terms relative to values estimated for the vehicle travelling at the speed limit at the safety critical event. The relative accident risk in response to a safety critical situation shows a partial speed dependent reaction phase and a speed-squared dependent braking phase and ranges from 1.3 to 2.8. The speed-squared dependent accident severity measure ranges from 1.4 to 7.3 at pre-impact speeds. The relative accident severity risk shows speed squared to speed cubed dependency components during the reaction phase and a speed to the power of four dependent braking phase and ranges from 2.3 to 22.8. In eight cases the collision would have been avoided had the motorcyclist been travelling at the speed limit at the critical point and in the other eight cases the relative accident severity at impact ranged from 1.4 to 17.2. The speed-squared versus stopping distance domain provides an informative parameter space for considering the accident risk and accident severity dimensions of road user accidents., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Road accident fatalities and accident severity costs have become top priorities and concerns for Chinese policymakers. Understanding the principal factors that explain accident severity is considered to be the first step towards the adequate design of an accident prevention strategy. In this paper, we examine the contribution of various types of factors (vehicle, driver and others) in explaining accident severity in China. Unlike previous studies, the analysis gives a particular focus on fatal accidents. Using a large sample of 405,177 observations for 4-wheeled vehicles in the year 2017 and various statistical and econometrics approaches (e.g., OLS, quantile regression and extreme value theory), the results show that the factors explaining the severity of accidents differs significantly between normal and extreme severity accidents, e.g. across quantiles. Interestingly, we find that the gender factor is only significant for fatal accidents. In particular, the analysis shows that male drivers have an increased likelihood of extreme risk taking. On the basis of these empirical findings, a new ratemaking approach that aims to improve road safety and prevention is discussed and proposed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

While the bus is generally considered to be a relatively safe means of transportation, the property losses and casualties caused by bus accidents, especially fatal ones, are far from negligible. The reasons for a driver to incur fatalities are different in each case, and it is essential to discover the underlying risk factors of bus fatality severity for different types of drivers in order to improve bus safety. The current study investigates the underlying risk factors of fatal bus accident severity to different types of drivers in the U.S. by estimating an ordered logistic model. Data for the analysis are retrieved from the Buses Involved in Fatal Accidents (BIFA) database from the USA for the years 2006-2010. Accidents are divided into three levels by counting their equivalent fatalities, and the drivers are classified into three clusters by the K-means cluster analysis. The analysis shows that some risk factors have the same impact on different types of drivers, they are: (a) season; (b) day of week; (c) time period; (d) number of vehicles involved; (e) land use; (f) manner of collision; (g) speed limit; (h) snow or ice surface condition; (i) school bus; (j) bus type and seating capacity; (k) driver's age; (l) driver's gender; (m) risky behaviors; and (n) restraint system. Results also show that some risk factors only have impact on the "young and elder drivers with history of traffic violations", they are: (a) section type; (b) number of lanes per direction; (c) roadway profile; (d) wet road surface; and (e) cyclist-bus accident. Notably, history of traffic violations has different impact on different types of bus drivers., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The number of pedestrian-motor vehicle accidents and pedestrian deaths in China surged in recent years. However, a large scale empirical research on pedestrian traffic crashes in China is lacking. In this study, we identify significant risk factors associated with fault and severity in pedestrian-motor vehicle accidents. Risk factors in several different dimensions, including pedestrian, driver, vehicle, road and environmental factors, are considered. We analyze 6967 pedestrian traffic accident reports for the period 2006-2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report. Pedestrian traffic crashes have a unique inevitability and particular high risk, due to pedestrians' fragility, slow movement and lack of lighting equipment. The empirical analysis of the present study has the following policy implications. First, traffic crashes in which pedestrians are at fault are more likely to cause serious injuries or death, suggesting that relevant agencies should pay attention to measures that prevent pedestrians from violating traffic rules. Second, both the attention to elderly pedestrians, male and experienced drivers, the penalty to drunk driving, speeding, driving without a driver's license and other violation behaviors should be strengthened. Third, vehicle safety inspections and safety training sessions for truck drivers should be reinforced. Fourth, improving the road conditions and road lighting at night are important measures in reducing the probability of accident casualties. Fifth, specific road safety campaigns in rural areas, and education programs especially for young children and teens should be developed and promoted. Moreover, we reveal a country-specific factor, hukou, which has significant effect on the severity in pedestrian accidents due to the discrepancy in the level of social insurance/security, suggesting that equal social security level among urban and rural people should be set up. In addition, establishing a comprehensive liability distribution system for non-urban areas and roadways will be conducive to both pedestrians' and drivers' voluntary compliance with traffic rules., (Copyright © 2014. Published by Elsevier Ltd.)

The study examines the determinants of fishing vessel accident severity in the Northeastern United States using vessel accident data from the U.S. Coast Guard for 2001-2008. Vessel damage and crew injury severity equations were estimated separately utilizing the ordered probit model. The results suggest that fishing vessel accident severity is significantly affected by several types of accidents. Vessel damage severity is positively associated with loss of stability, sinking, daytime wind speed, vessel age, and distance to shore. Vessel damage severity is negatively associated with vessel size and daytime sea level pressure. Crew injury severity is also positively related to the loss of vessel stability and sinking., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

With the recent economic boom in China, vehicle volume and the number of traffic accident fatalities have become the highest in the world. Meanwhile, traffic accidents have become the leading cause of death in China. Systematically analyzing road safety data from different perspectives and applying empirical methods/implementing proper measures to reduce the fatality rate will be an urgent and challenging task for China in the coming years. In this study, we analyze the traffic accident data for the period 2006-2010 in Guangdong Province, China. These data, extracted from the Traffic Management Sector-Specific Incident Case Data Report, are the only officially available and reliable source of traffic accident data (with a sample size>7000 per year). In particular, we focus on two outcome measures: traffic violations and accident severity. Human, vehicle, road and environmental risk factors are considered. First, the results establish the role of traffic violations as one of the major risks threatening road safety. An immediate implication is: if the traffic violation rate could be reduced or controlled successfully, then the rate of serious injuries and fatalities would be reduced accordingly. Second, specific risk factors associated with traffic violations and accident severity are determined. Accordingly, to reduce traffic accident incidence and fatality rates, measures such as traffic regulations and legislation-targeting different vehicle types/driver groups with respect to the various human, vehicle and environment risk factors-are needed. Such measures could include road safety programs for targeted driver groups, focused enforcement of traffic regulations and road/transport facility improvements. Data analysis results arising from this study will shed lights on the development of similar (adjusted) measures to reduce traffic violations and/or accident fatalities and injuries, and to promote road safety in other regions., (Copyright © 2013 Elsevier Ltd. All rights reserved.)