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... statistics illustrate the fact that when it comes to crash severity, ROR crashes tend to be more severe. The percentage of different injury crash- es to that of total single vehicle ROR crashes in Kansas is presented in Figure 1. The figure shows that the percen- tage of fatal and incapacitating crashes remains relatively constant over the years, while the percentage of non- incapacitating crashes fluctuates with the highest percen- tage observed in 2006. ...
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Los combates de alta intensidad en Donbass en la segunda mitad de 2014 y primeros meses de 2015 ofrecen numerosas lecciones para las fuerzas terrestres. Este documento centra su atención en tres aspectos: artillería, carros de combate y vehículos blindados.
Citations
... The results implied that senior drivers (>56 years) had an 8.6% higher probability of being SIK in the SVAs relative to middleaged drivers (26-40 years). Similar result was observed in the United States (Adanu et al., 2018;Chen et al., 2016;Dissanayake and Roy, 2014;Kim et al., 2013;Savolainen and Mannering, 2007;Schneider et al., 2009), Singapore (Zhou and Chin, 2019), India (Sivasankaran et al., 2021), and Thailand (Se et al., 2020). Some studies reported that young drivers were linked to more severe SVA injuries. ...
... Se et al. (2020) found that the injury severity of middle-aged drivers increased with a marginal effect of 0.016 when the SVAs occurred during night-time without light. This result was similar to the findings of Martensen and Dupont (2013), Dissanayake andRoy (2014), andOsman et al. (2018). Some studies also reported that the morning time was associated with higher SVA severity, such as Wen and Xue (2020) in China, Zhou and Chin (2019) in Singapore, Se et al. (2021) in Thailand, Pervez et al. (2022) in Pakistan, and Islam et al. (2014) and Li and Fan (2019) in the United States. ...
... In terms of accident-specific characteristics, speeding (Savolainen and Mannering, 2007;Peng and Boyle, 2012;Lee and Li, 2014;Osman et al., 2018;Rahimi et al., 2020;Yu and Long, 2021), overtaking (Anarkooli et al., 2017;Islam et al., 2016), distracted driving (Peng and Boyle, 2012;Adanu et al., 2018;Osman et al., 2018), and drunken driving variables (Dissanayake and Roy, 2014;Mashhadi et al., 2018;Se et al., 2020;Wen and Xue, 2020) increased the SVA severity. Chen et al. (2020) reported that the rollover SVA severity was 3.65 times higher than that of nonfixed object-type collisions. ...
Approximately 30% of total road traffic accidents in India are single-vehicle accidents (SVAs). The main objective of the present study is to evaluate the individual and interacted effects of risk factors on SVA severity. Using ten-year (2011-2020) accident data reported from Itanagar and Shillong, India, two sets of binary logistic regression models (model-without-interaction and model-with-interaction) were developed. The present study considered two-way and three-way interaction analysis in the model-with-interaction. Modelling results show that in the model-without-interaction, 7 categorical variables (18-24 age group, 25-40 age group, above 40 age group, heavy motor vehicle, 6 PM-12 AM, rushed driving, and rollover) in the Itanagar study area and 4 categorical variables (female, light motor vehicle, out-of-control, and Run-off-Road) in Shillong study area were shown statistically significant for the SVA severity. For the model-with-interaction, there were 12 significant interactions (7 two-way interactions and 5 three-way interactions) in the Itanagar study area and 7 significant interactions (5 two-way interactions and 2 three-way interactions) in the Shillong study area. These findings will be useful for transportation policy-makers while implementing correctives to improve road safety in these regions.
... A disaggregate model of road accident severity based on sequential logit models was presented by Nassar et al. (1994) to reveal the factors that affect the level of damage experienced by individuals involved in single-vehicle, two-vehicle and multi-vehicle road crashes, namely: accident dynamics, seating position, vehicle condition, vehicle size, driver condition, and driver action. Dissanayake and Roy (2014) employed binary logit models to prove that different driver, vehicle, road, crash, and environment related factors influence crash severity, concluding that run-off-road crashes typically tend to be more severe than other types of crashes. Similarly, Okafor et al. (2023) found that single-vehicle left run-off crashes are more likely to result in severe crashes compared to right single-vehicle left run-off and that male drivers, Driving Under Influence (DUI), motorcycles, and dry road surfaces were significant contributing factors to their severities. ...
Road traffic in urban areas is highly prone to the occurrence of road crashes, while crash severity level differs at the city level and in relation to the leading cause. In the case of urban sustainable environments, strategic planning and development measures have been implemented for safer mobility by effective traffic management and road user education. Still, in crowded cities, where mobility and transit traffic are major factors, road crashes are the antithesis of road safety and they occur in simple or complex circumstances mostly related to road users and road infrastructure. The aim of this study was to define the severity level of road traffic crashes in the cities of Romania during the reference period 2008 - 2019, before the COVID-19 pandemic, while highlighting the leading causes of road crashes in urban areas that determine their highest occurrence and severity level. Official crash data records provided by the general Directorate of the Romanian Police were employed. The general severity degree of road crashes was calculated, particularly in relation to the first five leading causes, and cities were ranked accordingly. Results are rendered at the local level, for all 319 cities and towns in Romania, by the five leading causes and by severity level, comparatively for the years 2008 and 2019. Synthetically, we pinpoint the Romanian cities that are most affected by this phenomenon. This could help local and national decision-makers direct their focus towards mitigating the negative effects of the frequent crash triggering factors, decrease road crash severity levels and create customized measures for better traffic management and increased road safety at the local level.
... Data includes Year, Speed Limit, Weather-condition, Schoolzone, Humps, hospital zone, road type, men at work, Accidents and Injuries. [9][10] ...
Traffic collisions are one of the world's 14 most crucial issues right now since they cause countless fatalities, severe injuries, and financial losses every year. The difficulty of creating accurate forecasting models, Traffic accident severity is important for transportation networks. This study creates models to choose a number of important features and build a model for classifying injury severity. These models are produced utilizing various Machine Learning techniques. The data on traffic accidents, trained and methods for unsupervised machine learning are also used. The primary aim is to draw a connection between the many kinds of injuries and traffic accident types. The study's conclusions imply that unsupervised learning methods may be effective in predicting the severity of harm from traffic accidents.
... Single-vehicle crashes occur for a variety of reasons, including vehicle overturning, collisions with various roadside features such as trees, guardrails, poles, sign supports, utility poles, fences, kerb, and so on; and various driver-related factors such as alcohol involvement, speeding, medical conditions, falling asleep, careless driving, and so on. The financial costs of these kinds of crashes are enormous [5]. Different organizations have acknowledged the importance of the roadside safety problem, and attempts have been made to eliminate the types of errors most likely to cause roadside crashes. ...
... Poisson regression and negative binomial (NB) regression are two techniques for predicting count outcomes that occur within a particular location or time range [4]. The Poisson and NB regressions do not assume that the residuals are normally distributed and have a constant variance [5].The conditional mean and variance of the count distribution are assumed to be equal in Poisson regression [11]-a condition referred to as equi-dispersion [4]. Overdispersion occurs when the variance of the count outcome is greater than the mean [4]. ...
For decades, researchers have been working to improve our understanding of the elements that influence the chance of vehicle accidents. One of the most difficult issues in traffic safety is identifying the relationship between motor vehicle crashes and roadway geometry elements, characteristics of road user, and vehicle characteristics. Traffic flow, the presence of a junction, and the presence of a physical division between carriageways were the most critical variables in distinguishing between single and multivehicle crashes. To get more insight into the factors that are involved in the single-vehicle crashes, this paper investigates single-vehicle crashes that were reported for Vadodara, India. Poisson and Negative Binomial regression models were developed for single-vehicle crash prediction. The model estimation parameters indicate a significant association of single-vehicle crashes with road geometry like the number of lanes, traffic flow, and length of the road segment. It has also been shown that the on-street parking and access density has not much to do with the single-vehicle crash that occurred at the roadway. Practical application: At the planning stage smaller road segment size with less wide roads will result in lower traffic flow ultimately resulting in less single-vehicle crashes.KeywordsSingle-vehicle crashNegative Binomial regressionPoisson regressionMid-block crashesRoad network digitizationAccess density
... It is also necessary to recognize that the results could reflect the weak segregation of motorized from non-motorized modes of transport in Cali (pedestrians and cyclists) [45] or, perhaps, reveal deficiencies in the geometric design of roads [46], the presence of fixed objects [47] near traffic lanes, and deficiencies in victim care services (ambulances and intensive care units) [48]. ...
The mobility plan and the road infrastructure works implemented, together with the Bus Rapid Transit (BRT) connected bus system in its first two phases, generated optimistic expectations about the reduction of lethal crashes in the city. This research studies the relationship between investments in transportation infrastructure in the city and the distribution of traffic fatalities. Although it is not strictly speaking an impact assessment, the approach we propose performs geostatistical contrasts between intervened and non-intervened areas, using a geographically weighted model that attempts to model the spatial variability of the factors associated with the intra-urban road traffic crash rate, controlling for infrastructure interventions and some proxy indicators of urban structure. The findings reveal that fatalities decreased in areas both with and without intervention. Despite the expectation of reducing fatal injuries, the differential effects of the interventions were relatively small. The risk of road traffic crashes was even increased in critical points of the city with recurrent lethal crashes. The effects of road interventions on fatal road traffic crashes in Cali did not correspond to the high social and economic costs involved in the BRT system and the work plan.
... This type of crash occurs when a vehicle in motion leaves the road, collides with a tree, pole, or other natural or artificial objects, or overturns on non-traversable terrain. In the United States, ROR crashes are a major cause of severe injuries and fatalities, especially among vehicle occupants (Dissanayake and Roy 2014). The Fatality Analysis and Reporting System (FARS) data from 2014-2019 showed that 37.8% of all vehicles involved in fatal crashes were single vehicles (FARS 2022) and ROR crashes contribute to 50% of all SV crashes (National Highway Traffic Safety Administration 2017). ...
... Four variables (male drivers, DUI, motorcycle, and dry road surface) were significant in the two model results. DUI, motorcycle, and dry road surface increased the likelihood of severe crashes and were consistent with previous studies (Liu and Ye 2011;Dissanayake and Roy 2014). Typically, drivers are more likely to travel at higher speeds on dry road surfaces, which is associated with severe crash outcomes (Liu and Ye 2011). ...
Objectives:
Single vehicle (SV) run-off-road crashes are a major cause of severe injury and fatality. Such crashes can result in different levels of severity depending on the direction (i.e., left or right) in which the vehicle runs off the road. This paper investigated the factors contributing to the crash severities of right run-off-road (R-ROR) and left run-off-road (L-ROR) SV crashes.
Methods:
The study used SV crash data from the City of Charlotte, North Carolina, covering 2014 to 2017. Two separate random parameter ordered logit (RPOL) models were developed to estimate the contributing factors to R-ROR and L-ROR SV crash severities. The impact of the explanatory variables on the crash severity outcomes was quantified using the models' direct pseudo-elasticities.
Results:
The model results showed that male drivers, Driving Under Influence (DUI), motorcycles, and dry road surfaces were significant contributing factors to R-ROR and L-ROR SV crash severities. Specifically for the R-ROR model, speeding, reckless driving, 1-2 lanes, and older drivers increased crash severity. For the L-ROR model, phone distraction, crossed centerline/median, 3-4 lanes, rain, and dark unlighted roadway increased crash severity.
Conclusions:
Based on the estimated parameters for the common significant variables in the two models, it was inferred that L-ROR SV crashes are more likely to result in severe crashes compared to R-ROR SV crashes. Hence, this study contributes to the literature on ROR SV crashes by providing additional insight into contextual factors influencing ROR crash severity for more effective countermeasures.
... For instance, previous injury severity analyses have used methods such as ordered probit [29], ordered logit [30], multinomial logit [31], mixed logit [32,33], random parameters with heterogeneity in means [34,35], random parameters with heterogeneity in means and variances [36], latent class analysis [37][38][39], Markov switching models [40], Markov switching with random parameters [41], and bivariate/multivariate models with random parameters [42,43]. In addition, several other studies have explored the relationships between crash factors and crash outcomes using a binary logit modeling approach [44][45][46][47][48]. As noted in the data description section, two severity categories (injury and PDO) were considered for this study. ...
Although the school bus is considered a safe form of transportation, school bus crashes are a major safety concern. School bus crashes are a result of driver error of either the at-fault school bus or another at-fault vehicle (where the school bus is not at fault). To examine the risk factors associated with school bus related crashes and crash outcomes, this study segments and develops two binary logit models for each school bus crash type. A total of 1702 school bus related crashes recorded between 2009 and 2016 were used to estimate the model. According to the model results, sideswipe collisions were less likely to result in injury outcomes for at-fault school buses than rear-end and side-impact collisions. Speeding, driving impaired or under the influence, and negotiating a curve all have significant positive associations with injury outcomes in not-at-fault school bus crashes. This study’s practical implications include enforcing the school bus safe driving guidelines, training programs for school bus drivers that include elements of nonroutine trips, training for crossing guards, and awareness programs for drivers of other vehicles to instill safe driving practices around school buses.
... A Pearson correlation method was therefore used to identify and remove highly correlated variables. As indicated in previous studies, a correlation value of 0.5 has been used as a threshold while identifying highly correlated variables (50,57). The proportion of people with less than $30,000 annual income variable was highly correlated with the proportion of households without a vehicle (0.7), and the proportion of people with limited English proficiency (0.6), and was removed from further analysis. ...
The objective of this study was to identify pedestrian crash hotspots and assess the built environment to identify factors that increase the probability of a roadway corridor becoming a hotspot. Therefore, this research attempted to identify the pedestrian hotspots and then answer the question, do locations with a “specific built environment” have a higher probability of becoming a pedestrian crash hotspot? The study used 4 years of pedestrian crash data (2011 to 2014) from Miami-Dade County, one of the top three leading counties in the United States with the highest pedestrian fatalities. Pedestrian crash hotspots were first identified in the ArcGIS environment, integrating spatial analysis. The Bayesian complementary log-log (cloglog) model was then used to develop a hotspot risk prediction model, in which the likelihood of a corridor becoming a hotspot was linked to the built environment, and to demographic and socioeconomic factors. The density of bus stops, shopping centers, healthcare facilities, hotels, alcohol sales establishments, households without vehicles, traffic volume, presence of sidewalks, and presence of medians were found to significantly increase the likelihood of a corridor becoming a pedestrian crash hotspot. The methodological framework and findings of this study could be used while developing site-specific proactive measures to improve pedestrian safety.
... 8 The most important issues in the road safety field are predicting crash frequency and crash injury severity. Modeling and prediction of vehicle-pedestrian crashes, [9][10][11][12][13][14] vehicle-bicycle crashes, 15-18 motorcycle crashes, [19][20][21][22][23][24][25][26] and single-vehicle crashes [27][28][29][30][31][32][33] are some related studies that have been conducted to examine crash injury severity using statistical approaches or machine learning methods. Machine learning methods relatively produce better fitting than statistical models for crash injury severity prediction. ...
Road accident is one of the important problems in the world which caused large number of deaths. In a road crash dataset, the fatal crash samples, often constitute very small proportion in comparison with non-fatal crash samples. Accurate prediction of fatal crashes, as a minority class, is one of the important challenges in such imbalanced sample distribution in the most of machine learning algorithms. This study introduced data leveling methods based on two metaheuristic optimization algorithms (biogeography-based optimization and invasive weed optimization) to obtain more balanced data. Then, three machine learning algorithms including decision tree, support vector machine (SVM) and k-nearest neighbor were applied for obtained balanced dataset. Performances of the prepared models were evaluated by improving the accuracy of the models in detecting the fatal crashes. It is found that data leveling methods of imbalanced dataset with metaheuristic algorithms improve the performance of crash prediction models in detecting fatal crashes especially in SVM algorithm up to 100% compared to previous studies. Also, results of sensitivity analysis on the developed model represented that head-on crashes, curved roads, and large type vehicles can increase the probability of fatal crashes up to 27.2%, 29%, and 36.8% at high posted speed limit, respectively. Also, two-vehicle crashes are much more likely to be involved in fatal crashes than single-vehicle crashes.
... To check whether there is any collinearity between explanatory variables, a correlation matrix produced by proc corr in SAS was used. Once a variable was identified with correlation coefficient greater than 0.5, a linear regression model was used to further examine the variables (Dissanayake and Roy, 2014). Tolerance (TOL) and variance inflation factor (VIF) were generated by using proc reg in SAS software to check the correlation (SAS Institute, 2015). ...
Animal-vehicle crashes (AVCs) are a severe concern in the United States as well as in Wyoming. This study investigated the current trends of AVCs and crash rate per vehicle miles traveled (VMT) for the State of Wyoming using police-reported crash data for ten years collected from the Wyoming Department of Transportation (WYDOT). The study also examined different driver, vehicular, roadway, and environmental conditions related factors that had a strong association with AVCs. The logistic regression model was developed to check how the factors prevalent in AVCs influence the severity of AVCs. The results showed that the percentage of AVCs to that of total crashes ranged from around 15% to 22%. Among different animals involved in crashes, the majority were deer. AVCs were found to have two peaks: dawn and dusk. AVC rate was found to be the highest in November. Also, dark and unlit conditions had a strong association with AVCs. Most of the AVCs tended to occur when there were no adverse weather conditions. When the speed limit was examined, it was found that the AVC crash rate tended to increase when the posted speed limit was higher than 60 mph. AVC rate was also higher when the road surface condition was dry. Higher posted speed limit, younger drivers, and dry road surface were also found to increase the severity of AVCs. The results identified in this study will be helpful to identify effective countermeasures to reduce AVCs in Wyoming.
