Multiple-vehicle traffic accidents in Hong Kong

‘Multiple-vehicle traffic accident’ refers to a crash between two or more moving objects. Unlike single-vehicle accidents, not all drivers involving in a multiple-vehicle accident are responsible for the occurrence of the event. Accordingly, variables such as road type, speed limit and number of vehicles involved in the accident are expected to play a much more important role in association with injury severity in multiple-vehicle accidents. To study the factors influencing injury severity of multiple-vehicle traffic accidents, a population-based study was conducted. The traffic accident data was obtained from the Traffic Accident Data System (TRADS), which was developed by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. Multiple-vehicle traffic accidents (N = 10,630) occurring during the 2-year period 1999/2000 were considered. Potential risk factors such as district, human, vehicle, safety, environmental and site factors were examined. Categorizing injury severity into “fatal/serious” and “slight”, a stepwise logistic regression model was applied to the population data set. The district board, time of the accident, driver's gender, vehicle type, road type, speed limit and the number of vehicles involved are significant factors influencing the injury severity. Identification of risk factors for severe traffic accidents provides valuable information to help with new and improved road safety control measures.     

Traffic accidents involving fatigue driving and their extent of casualties

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a “silent killer.” Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006–2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.     

A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey

A retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police between January 2008 and December 2013 which are classified into three injury severity categories: fatal, injury, and no injury. Based on this classification, a multinomial logit analysis is performed to determine the risk factors affecting the severity of traffic injuries. The estimation results reveal that the following factors increase the probability of fatal injuries: drivers over the age of 65; primary-educated drivers; single-vehicle accidents; accidents occurring on state routes, highways or provincial roads; and the presence of pedestrian crosswalks. The results also indicate that accidents involving cars or private vehicles or those occurring during the evening peak, under clear weather conditions, on local city streets or in the presence of traffic lights decrease the probability of fatal injuries. This study comprises the most comprehensive database ever created for a Turkish sample. This study is also the first attempt to use an unordered response model to determine risk factors influencing the severity of traffic injuries in Turkey.     

Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents.

The impact that large trucks have on accident severity has long been a concern in the accident analysis literature. One important measure of accident severity is the most severely injured occupant in the vehicle. Such data are routinely collected in state accident data files in the U.S. Among the many risk factors that determine the most severe level of injury sustained by vehicle occupants, the number of occupants in the vehicle is an important factor. These effects can be significant because vehicles with higher occupancies have an increased likelihood of having someone seriously injured. This paper studies the occupancy/injury severity relationship using Washington State accident data. The effects of large trucks, which are shown to have a significant impact on the most severely injured vehicle occupant, are accounted for by separately estimating nested logit models for truck-involved accidents and for non-truck-involved accidents. The estimation results uncover important relationships between various risk factors and occupant injury. In addition, by comparing the accident characteristics between truck-involved accidents and non-truck-involved accidents, the risk factors unique to large trucks are identified along with the relative importance of such factors. The findings of this study demonstrate that nested logit modeling, which is able to take into account vehicle occupancy effects and identify a broad range of factors that influence occupant injury, is a promising methodological approach.     

An analysis of accident data for franchised public buses in Hong Kong

This paper analyses data on accidents involving franchised public buses operating in Hong Kong. The data were obtained from the Royal Hong Kong Police, the Hong Kong Government Transport Department, the two major franchised bus operators and international sources. The analysis includes an international comparison of accidents with emphasis on the situation in Hong Kong compared to urban areas in the United Kingdom. An attempt has been made to identify the characteristics of bus accidents; accident incidence has been related to time of day, day of the week, time of year, weather conditions, driver's age and experience, hours on duty and policy-reported cause. The results indicate that Hong Kong has a high accident rate compared to Japan, the U.K. and the U.S.A., with particularly high pedestrian involvement rates. Bus accidents peak at around 9:00 AM and 4:00 PM but the accident rate is high throughout the day. Monday and Saturday appear to have a higher than average accident rate. The variability of accident rate throughout the year does not seem to be significant and the accident rate does not appear to be influenced by weather conditions. Older, more experienced drivers generally have a safer driving record than their younger, less experienced colleagues. Accident occurrence is related to the time the driver has been on duty. The paper questions the reliability of police-reported accident causation data and suggests improvements in the design of the accident report form and in the training of police investigators. The relevance of the Hong Kong study for accident research in general is also discussed.     

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.     

Bayes classifiers for imbalanced traffic accidents datasets

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009–2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.     

Analyzing the severity of accidents on the German Autobahn

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.     

Evaluation and optimization of chromium removal from tannery effluent by microemulsion in the Morris extractor

A multidisciplinary Road Accident Analysis Group with the objective of conducting in-depth investigations of specific types of accidents has existed in Denmark for some years. The group has analysed head-on collisions, left-turn accidents, truck accidents and single vehicle accidents. The data collection included police reports, the group's investigation of accident sites and vehicles involved, and interviews with the involved road users and witnesses. The main accident factors in the head-on collisions and in the single vehicle accidents were excessive speed, drunk driving and driving under the influence of illegal drugs. The primary accident factors in left-turn accidents were attention errors or misjudging the amount of time available to complete the left turn. In the truck accidents insufficient searching for visual information as well as speeding were major factors. For all the accident themes the primary injury factor was failure to wear seat- belts. The multidisciplinary approach has provided a rather precise knowledge of the contributing factors leading up to the accident. The method requires a lot of resources, which is a limiting factor for the number of accidents to be analysed in this way. However, the method is suitable for analysis of common occurring or very serious types of accidents.     

Vehicle occupant injury severity on highways: An empirical investigation

Accident severity analysis is important to both researchers and practitioners because of its implications in accident cost estimation, external cost estimation and road safety. Although much research has been done to explore the factors influencing crash-injury severity, few studies have investigated the association between severity and traffic characteristics collected real-time during the time the accident occurred. We apply a random parameters ordered probit model to explore the influence of speed and traffic volume on the injury level sustained by vehicle occupants involved in accidents on the A4–A86 junction in the Paris region. Results indicate that increased traffic volume has a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions.     

Economic costs of traffic accidents in Jordan.

The objectives of this study were to estimate the economic costs of traffic accidents in Jordan during the year of 1996 and to derive unit accident costs for various accident severity levels. The related data were acquired from different sources, including traffic police records, insurance companies, private hospitals and medical centers. In this study, a framework for applying unit casualty class costs, unit property damage cost, as well as police activities and insurance administration costs to accidents of various severity levels was suggested. The loss-of-output, the loss quality of life, the community and family losses, the temporary and permanent losses, and hospitalization and medical treatment costs were estimated in computing the unit cost for fatalities or injuries of different casualty classes. The vehicle repair cost, detention period cost, and public and private costs were accounted for in estimating the unit cost of property damages. The results indicate that the 1996 traffic accidents cost the country about JD 103 million ($US 146.3 million).     

Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents

This research explores differences in injury severity between male and female drivers in single and two-vehicle accidents involving passenger cars, pickups, sport-utility vehicles (SUVs), and minivans. Separate multivariate multinomial logit models of injury severity are estimated for male and female drivers. The models predict the probability of four injury severity outcomes: no injury (property damage only), possible injury, evident injury, and fatal/disabling injury. The models are conditioned on driver gender and the number and type of vehicles involved in the accident. The conditional structure avoids bias caused by men and women's different reporting rates, choices of vehicle type, and their different rates of participation as drivers, which would affect a joint model of all crashes. We found variables that have opposite effects for the genders, such as striking a barrier or a guardrail, and crashing while starting a vehicle. The results suggest there are important behavioral and physiological differences between male and female drivers that must be explored further and addressed in vehicle and roadway design.     

The effect of roundabout design features on cyclist accident rate

Roundabouts are known to result in fewer traffic accidents than traditional intersections. However, this is to a lesser degree true for bicycles than for vehicles. In this paper, we aimed at establishing statistical relationships through Poisson regression and logistic regression analyses between yearly rate of cyclist accidents on one hand and roundabout geometry, age and traffic volume (vehicles and cyclists) on the other. We related all roundabout cyclist accidents recorded by the hospital emergency department of the town of Odense, Denmark, through the years 1999-2003 (N=171) to various geometric features, age and traffic volume of all roundabouts on the Danish island of Funen (N=88). Cyclist and vehicle volumes turned out to be significant predictors in most of our models-the higher the volumes, the more accidents. Moreover, potential vehicle speed was a significant predictor, and so was age of the roundabout-older roundabouts related to more accidents and higher accident probability. Excluding 48 single cyclist accidents strengthened the relationship between accidents on one hand and vehicle and cyclist volume and potential vehicle speed on the other. This stresses the significance of speed and traffic volume for traffic accidents with more than one partner involved. The 48 single cyclist accidents were significantly related to the traffic volume of cyclists only. Due to our limited number of observations, the models should be regarded as indicative.     

Analysis of traffic injury severity: an application of non-parametric classification tree techniques

Statistical regression models, such as logit or ordered probit/logit models, have been widely employed to analyze injury severity of traffic accidents. However, most regression models have their own model assumptions and pre-defined underlying relationships between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimations of injury likelihood. The classification and regression tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study uses the 2001 accident data for Taipei, Taiwan. A CART model was developed to establish the relationship between injury severity and driver/vehicle characteristics, highway/environmental variables and accident variables. The results indicate that the most important variable associated with crash severity is the vehicle type. Pedestrians, motorcycle and bicycle riders are identified to have higher risks of being injured than other types of vehicle drivers in traffic accidents.     

A study of reported injury accidents among novice motorcycle riders in a Scottish region

A study is reported of the effect of sex, age, cubic capacity, and training on the rate of reported injury accidents in a cohort of 304 first time learner motorcycle riders resident in the Lothian and Borders of Scotland in 1983. Motorcycle in this paper includes all types of registerable two wheeled motor vehicle. Injury accidents as reported by the police were observed in this cohort over an average period of one year. The overall reported injury accident rate within the cohort was 8.2 per hundred riders. This rate does not seem to be markedly different to the Scottish rate for all riders. It was found that the cubic capacity of the motorcycle was the single most important risk factor of the four studied. The risk was disproportionately high in the 200+ cc category. Lower reported injury accident rates were observed for females and trained riders but these differences did not reach statistical significance mainly due to the low numbers of these two categories within the cohort. Contrary to popular assumption, younger riders within this cohort did not have higher injury accidents. A large proportion of the riders who had been involved in injury accidents within the cohort and who had registered 50 cc motorcycles were found to be riding higher capacity (mainly 200+ cc) motorcycles at the time of accident. There was a very low uptake of motorcycle training (7.3%) by the cohort. Approximately 15% of the cohort was female, a higher percentage than those reported by other studies.     

Modeling secondary accidents identified by traffic shock waves

The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident.     

Self-reported motorcycle riding behaviour among school children in India

Studies on traffic accidents among underage users of motorcycles are seldom seen in literature. This study was done in Yamunanagar, India where boys as young as 8 years ride motorcycles. It attempts to find out the behavioural and non-behavioural factors leading to motorcycle use and the predisposition to accidents among male school children aged between 10 and 16 years. A questionnaire was used to evaluate those factors among 1760 subjects in 38 schools. Fifteen percent of subjects had had an accident while riding motorcycle. Most of the behavioural and all the non-behavioural factors have a statistically significant influence on accident proneness. Aggressive behaviour and previous encounter with the police are the two strong predictors of accidents (p<0.001). Children as riders are exposed to higher risks of accident and longer life with disability. It also explains how these children behaviourally take up adult roles and seek adult risk taking attitudes. The implications of child motorcycle riders upon children themselves and on the society are discussed for a greater discourse on road safety motorcycle riding policy and to highlight the behavioural and non-behavioural factors that are associated with traffic accidents.     

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.     

The epidemiology of fatal motor vehicle accidents in Kuwait

The main objectives of this study are to present data on fatal motor vehicle accidents in Kuwait with a view to find out the principal epidemiological features, establish a baseline for future evaluation, use international death rates to compare Kuwait with other countries and offer specific recommendations as to how the risks involved can be reduced. The epidemiology of all fatal accidents which had occurred in Kuwait between 1 January 1977 and 31 December 1978 are analysed. This two-year series, which includes 726 accidents and represents 1.7% of all types of accidents, resulted in 803 deaths (7.3% of the national deaths toll) and 1175 injuries. The death rates per hundred million vehicle kilometers of travel, per 100,000 registered motor vehicles and per 100,000 resident population in 1978 were about 8.5, 84.8 and 31.7 respectively. These figures reveal significantly higher rates than in some industrialized countries, thus ranking motor vehicle accidents among the four leading causes of death and probably as the commonest single cause of death in Kuwait. The study shows that children under the age of 15 yr contributed 26.2% of all motor vehicle fatalities. 40.9% of pedestrian deaths (55.2% of the series), 25% of pedal-cyclist deaths, 6.3% of motorcyclist deaths and 1.2% of driver deaths. It also confirms the importance of the motor vehicle-pedestrian type of accident (58.3%) and the over-representation of buses, trucks and motorcycles with respect to involvement in fatal accidents as compared to their relative ratios of registered vehicles. The principal recommendations call for the formation of a National Council for Road Safety, the establishment of intensive roadside care, traffic education of road users and at risk groups, improvement of the environment and the firm enforcement of the law to curb the toll of this killer disease in a traditional country undergoing rapid modernization like Kuwait.     

Model of injury severity allowing for different gradings of severity: Some applications using the British road accident data

A difficulty in using the British national road accident data in the analysis of injury severity is that some systematic variations exist in the grading of injury severity. A statistical method of allowing for this variability is being developed for use when analysing the severity of injury sustained in different circumstances and the effect of injury-reducing measures on severity, with the aim of increasing the sensitivity of such analyses. The method being developed is based on the exponential model of distribution of severity proposed by Hutchinson. Applications of the method to several kinds of accidents not involving pedestrians are presented: those involving a single vehicle, two vehicles head-on, a motor-cycle only and a motor-cycle with one or two other vehicles. The severity of the resulting injuries under four sets of circumstances—day-urban, day-rural, night-urban and night-rural—are investigated. The data for 1972 were used and five police force areas were chosen. The results from this method are then compared with those given by analysis of some conventional measures of injury severity.