Formulating accident occurrence as a survival process

A conceptual framework for accident occurrence is developed based on the principle of the driver as an information processor. The framework underlies the development of a modeling approach that is consistent with the definition of exposure to risk as a repeated trial. Survival theory is proposed as a statistical technique that is consistent with the conceptual structure and allows the exploration of a wide range of factors that contribute to highway operating risk. This survival model of accident occurrence is developed at a disaggregate level, allowing safety researchers to broaden the scope of studies which may be limited by the use of traditional aggregate approaches. An application of the approach to motor carrier safety is discussed as are potential applications to a variety of transportation industries. Lastly, a typology of highway safety research methodologies is developed to compare the properties of four safety methodologies: laboratory experiments, on-the-road studies, multidisciplinary accident investigations, and correlational studies. The survival theory formulation has a mathematical structure that is compatible with each safety methodology, so it may facilitate the integration of findings across methodologies.     

Multilevel analysis in road safety research

Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (i) allow improving the fit of the model to the data, (ii) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (iii) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application.     

The effect of daily-activity patterns on crash involvement

The main purpose of this study is to analyze the effect of daily-activity and travel patterns on the risk of crash involvement. To this end, we develop a model that integrates daily-activity and travel choices in a single framework, recognizing that these variables affect the risk of crashes. This model can therefore provide predictions of the expected changes in risk levels from the implementation of measures that affect the daily-activity patterns and the socio-economic characteristics of the population.The empirical analysis makes use of data collected during a household survey that includes crash information and trip diaries. The model is applied in a case study of an Arab town in Israel to analyze various transportation policies. The results of this research show that in addition to individuals’ demographic and socio-economic characteristics, their daily-activity and travel patterns also have an impact on the risk of being involved in car crashes. The case study showed the potential of this framework for analyzing the effect of various social and transportation policies on road safety. To the best of our knowledge, this is the first time such relationships have been tested by using a disaggregate model and the first time activity-based models have been used to analyze exposure to the risk of road crashes.     

Quantifying safety benefit of winter road maintenance: Accident frequency modeling

This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods.     

Road crash fatality rates in France: A comparison of road user types,taking account of travel practices

Background

Travel practices are changing: bicycle and motorized two-wheeler (MTW) use are rising in some of France’s large cities. These are cheaper modes of transport and therefore attractive at a time of economic crisis, but they also allow their users to avoid traffic congestion. At the same time, active transport modes such as walking and cycling are encouraged because they are beneficial to health and reduce pollution. It is therefore important to find out more about the road crash risks of the different modes of transport. To do this, we need to take account of the number of individuals who use each, and, even better, their travel levels.

Method

We estimated the exposure-based fatality rates for road traffic crashes in France, on the basis of the ratio between the number of fatalities and exposure to road accident risk. Fatality data were obtained from the French national police database of road traffic casualties in the period 2007–2008. Exposure data was estimated from the latest national household travel survey (ENTD) which was conducted from April 2007 to April 2008. Three quantities of travel were computed for each mode of transport: (1) the number of trips, (2) the distance traveled and (3) the time spent traveling. Annual fatality rates were assessed by road user type, age and sex.

Results

The overall annual fatality rates were 6.3 per 100 million trips, 5.8 per billion kilometers traveled and 0.20 per million hours spent traveling. The fatality rates differed according to road user type, age and sex. The risk of being killed was 20 to 32 times higher for motorized two-wheeler users than for car occupants. For cyclists, the risk of being killed, both on the basis of time spent traveling and the number of trips was about 1.5 times higher than for car occupants. Risk for pedestrians compared to car occupants was similar according to time spent traveling, lower according to the number of trips and higher according to the distance traveled. People from the 17–20 and 21–29 age groups and those aged 70 and over had the highest rates. Males had higher rates than females, by a factor of between 2 and 3.

Conclusion

When exposure is taken into account, the risks for motorized two-wheeler users are extremely high compared to other types of road user. This disparity can be explained by the combination of speed and a lack of protection (except for helmets). The differential is so great that prevention measures could probably not eliminate it. The question that arises is as follows: with regard to public health, should not the use of MTW, or at least of motorcycles, be deterred? The difference between the fatality risk of cyclists and of car occupants is much smaller (1.5 times higher); besides, there is much room for improvements in cyclist safety, for instance by increasing the use of helmets and conspicuity equipment. Traffic calming could also benefit cyclists, pedestrians and perhaps moped users.     

The alcohol-related accident risk in Germany: procedure, methods and results

This paper presents the first reliable estimation of the alcohol-related accident risk in Germany by comparing a representative sample of accidents to a representative sample of trips not leading to a crash. The information about the trips was taken from the German Roadside Survey 1992-1994 (n=9087) conducted in Unterfranken, part of Bavaria. These data were weighted according to a representative study of driving in Germany (KONTIV 89). The accident study comprises a representative sample of accidents in Unterfranken in 1993 (n=1968). Relating accident risk to BAC, the global risk function indicates an exponential increase of accident risk for BACs above 0.05%. Controlling for correlating factors leads to an overall lower estimation with, however, the same structure, indicating that alcohol is consumed by drivers in circumstances which further increase the risk introduced by alcohol. Analyzing the attributable risk (AR) shows that about 12% of all accidents are attributable to alcohol. Over 96% of these happen with BACs of 0.05% and above. Thus, measures aimed at reducing the alcohol-related accident risk must focus on larger BACs, especially of 0.08% and above.     

A new method for assessing the risk of accident associated with darkness

This paper presents a new method for assessing the risk of accidents associated with darkness. The method estimates the risk of accident associated with darkness in terms of an odds ratio, which is defined as follows: [(number of accidents in darkness in a given hour of the day)/(number of accidents in daylight in the same hour of the day)]/[(Number of accidents in a given comparison hour when the case hour is dark)/(Number of accidents in a given comparison hour when the case hour is in daylight)]. This estimate of the risk of accident associated with darkness does not require data on exposure, but relies on the count of accidents in the same pair of hours throughout the year. One of the hours is dark part of the year, but has daylight the rest of the year. The comparison hour, which has daylight the whole year, is used to control for seasonal variations. The aim of relying on the same pair of hours throughout the year is to minimise the influence of potentially confounding factors. Estimates of the risk of injury accidents associated with darkness are developed on the basis of accident data for Norway, Sweden and the Netherlands. It is found that the risk of an injury accident increases by nearly 30% in darkness in urban areas, by nearly 50% in rural areas, and by about 40% for urban and rural areas combined (adjusted estimate).     

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.     

Disaggregate analysis of road accident severities

A comprehensive understanding of the relationship between road accident occurrence and severity of consequences permits the formulation of safety measures that are most cost-effective. A disaggregate model of road accident severity based on sequential logit models is presented. The sequential binary approach is able to account for the dependency between different levels of severity. Factors that affect the level of damage experienced by individuals involved in road accidents include: accident dynamics, seating position, vehicle condition, vehicle size, driver condition and driver action. Separate models are calibrated for three accident situations: single-vehicle accidents, two-vehicle accidents and multi-vehicle accidents. Ontario road accident police reports are used to calibrate and validate the models. The results of a simple application of the models to a safety protocol involving the effectiveness of passenger restraint devices is presented.     

Multimodal injury risk analysis of road users at signalized and non-signalized intersections

This paper proposes a multimodal approach to study safety at intersections by simultaneously analysing the safety and flow outcomes for both motorized and non-motorized traffic. This study uses an extensive inventory of signalized and non-signalized intersections on the island of Montreal, Quebec, Canada, containing disaggregate motor-vehicle, cyclist and pedestrian flows, injury data, geometric design, traffic control and built environment characteristics in the vicinity of each intersection. Bayesian multivariate Poisson models are used to analyze the injury and traffic flow outcomes and to develop safety performance functions for each mode at both facilities. After model calibration, contributing injury frequency factors are identified. Injury frequency and injury risk measures are then generated to carry out a comparative study to identify which mode is at greatest risk at intersections in Montreal. Among other results, this study identified the significant effect that motor-vehicle traffic imposes on cyclist and pedestrian injury occurrence. Motor-vehicle traffic is the main risk determinant for all injury and intersection types. This highlights the need for safety improvements for cyclists and pedestrians who are, on average, at 14 and12 times greater risk than motorists, respectively, at signalized intersections. Aside from exposure measures, this work also identifies some geometric design and built environment characteristics affecting injury occurrence for cyclists, pedestrians and motor-vehicle occupants.     

Exposure and experience are a confounded nuisance in research on driver behaviour

Road accidents are usually the outcome of multicausal interface problems. Statistical controls therefore need to be equally complex, if accident analyses are to produce understanding of causal factors and lead to correction of contributory errors in driver behaviour. Crude control of accident data for distance travelled, time of day, etc. may identify high-risk groups. However, such controls could produce misleading results from research on individual differences in liability to error, because driving is basically self-paced and purposeful. Certain individuals will thus exhibit characteristically raised levels of risk exposure within the driving task, often as a result of pressures extrinsic to the traffic system. If the errors made by these individuals are to be identified, accident data must be corrected for such self-induced risk exposure, instead of this factor merely being used to “explain” accidents. This need seems most acute in experimental studies, where self-imposed demands and purposes of driving change over time and effects of exposure and experience are thus statistically confounded in accident data. However, control for self-induced risk exposure also seems important in studies of professional drivers' accidents and its neglect calls into question one of the basic assumptions of the “induced exposure” method of interpreting accident data.     

基于支持向量机的高速公路事故实时风险预测

以高速公路事故数据、交通流数据和天气数据为基础,以交通流为事故主要影响因素,建模预测高速公路事故实时风险。将事故记录作为病例组,采用病例对照方法来配对匹配实验样本,通过随机森林算法从众多变量中筛选出对事故风险影响最重要的10 个特征变量,以支持向量机建立模型预测事故实时风险。实验表明,通过随机森林筛选重要的特征变量,再使用支持向量机建模预测事故风险具有可行性,且以高斯核、Sigmoid核作为支持向量机的核函数比线性核函数和多项式核函数时分类准确性更高;其中,高斯核下支持向量机模型对事故风险预判的准确率达73.20%,对正常交通流的分类达91.44%。     

Scenario analysis of freight vehicle accident risks in Taiwan

This study develops a quantitative risk model by utilizing Generalized Linear Interactive Model (GLIM) to analyze the major freight vehicle accidents in Taiwan. Eight scenarios are established by interacting three categorical variables of driver ages, vehicle types and road types, each of which contains two levels. The database that consists of 2043 major accidents occurring between 1994 and 1998 in Taiwan is utilized to fit and calibrate the model parameters. The empirical results indicate that accident rates of freight vehicles in Taiwan were high in the scenarios involving trucks and non-freeway systems, while; accident consequences were severe in the scenarios involving mature drivers or non-freeway systems. Empirical evidences also show that there is no significant relationship between accident rates and accident consequences. This is to stress that safety studies that describe risk merely as accident rates rather than the combination of accident rates and consequences by definition might lead to biased risk perceptions. Finally, the study recommends using number of vehicle as an alternative of traffic exposure in commercial vehicle risk analysis. The merits of this would be that it is simple and thus reliable; meanwhile, the resulted risk that is termed as fatalities per vehicle could provide clear and direct policy implications for insurance practices and safety regulations.     

Using speeding detections and numbers of fatalities to estimate relative risk of a fatality for motorcyclists and car drivers

Precise estimation of the relative risk of motorcyclists being involved in a fatal accident compared to car drivers is difficult. Simple estimates based on the proportions of licenced drivers or riders that are killed in a fatal accident are biased as they do not take into account the exposure to risk. However, exposure is difficult to quantify. Here we adapt the ideas behind the well known induced exposure methods and use available summary data on speeding detections and fatalities for motorcycle riders and car drivers to estimate the relative risk of a fatality for motorcyclists compared to car drivers under mild assumptions. The method is applied to data on motorcycle riders and car drivers in Victoria, Australia in 2010 and a small simulation study is conducted.     

Toward an understanding of the impact of production pressure on safety performance in construction operations

It is not unusual to observe that actual schedule and quality performances are different from planned performances (e.g., schedule delay and rework) during a construction project. Such differences often result in production pressure (e.g., being pressed to work faster). Previous studies demonstrated that such production pressure negatively affects safety performance. However, the process by which production pressure influences safety performance, and to what extent, has not been fully investigated. As a result, the impact of production pressure has not been incorporated much into safety management in practice. In an effort to address this issue, this paper examines how production pressure relates to safety performance over time by identifying their feedback processes. A conceptual causal loop diagram is created to identify the relationship between schedule and quality performances (e.g., schedule delays and rework) and the components related to a safety program (e.g., workers’ perceptions of safety, safety training, safety supervision, and crew size). A case study is then experimentally undertaken to investigate this relationship with accident occurrence with the use of data collected from a construction site; the case study is used to build a System Dynamics (SD) model. The SD model, then, is validated through inequality statistics analysis. Sensitivity analysis and statistical screening techniques further permit an evaluation of the impact of the managerial components on accident occurrence. The results of the case study indicate that schedule delays and rework are the critical factors affecting accident occurrence for the monitored project.     

Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance

We conducted a study of approximately 16,000 drivers under the age of 30 that had purchased a pay-as-you-drive insurance policy, where their risk of being involved in a crash was analyzed from vehicle tracking data using a global positioning system. The comparison of novice vs. experienced young drivers shows that vehicle usage differs significantly between these groups and that the time to the first crash is shorter for those drivers with less experience. Driving at night and a higher proportion of speed limit violations reduces the time to the first crash for both novice and experienced young drivers, while urban driving reduces the distance traveled to the first crash for both groups. Gender differences are also observed in relation to the influence of driving patterns on the risk of accident. Nighttime driving reduces the time to the first accident in the case of women, but not for men. The risk of an accident increases with excessive speed, but the effect of speed is significantly higher for men than it is for women among the more experienced drivers.     

Multiday driving patterns and motor carrier accident risk: a disaggregate analysis.

A method has been developed to estimate the relative accident risk posed by different patterns of driving over a multiday period. The procedure explicitly considers whether a driver is on duty or off duty for each half hour of each day during the period of analysis. From a data set of over 1,000 drivers, nine distinct driving patterns are identified. Membership in the patterns is determined exclusively by the pattern of duty hours for seven consecutive days; for some drivers an accident occurred on the eighth day while others had no accident, therefore each pattern can be associated with a relative accident risk. Additional statistical modeling allowed the consideration, in addition to driving pattern, of driver age, experience with the firm, hours off duty prior to the last trip and hours driving on the last trip (either until the accident or successful completion of the trip). The finding of the modeling is that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during the early and late morning (e.g., midnight to 10 A.M.) has the highest accident risk while all seven other multiday patterns had indistinguishable risk. Consecutive hours driven also has a significant effect on accident risk: the first hour through the fourth hour having the lowest risk with a fluctuating increase in risk to a maximum beyond nine hours. Driver age and hours off duty immediately prior to a trip do not appear to affect accident risk significantly. These findings quantitatively assess the relative accident risk of multiday driving patterns using data from actual truck operations. Further research is recommended in the areas of refining model structures, adding explanatory variables (such as highway type), and testing more complex models.     

Measuring accident risk exposure for pedestrians in different micro-environments

Pedestrians are mainly exposed to the risk of road accident when crossing a road in urban areas. Traditionally in the road safety field, the risk of accident for pedestrian is estimated as a rate of accident involvement per unit of time spent on the road network. The objective of this research is to develop an approach of accident risk based on the concept of risk exposure used in environmental epidemiology, such as in the case of exposure to pollutants. This type of indicator would be useful for comparing the effects of urban transportation policy scenarios on pedestrian safety. The first step is to create an indicator of pedestrians’ exposure, which is based on motorised vehicles’ “concentration” by lane and also takes account of traffic speed and time spent to cross. This is applied to two specific micro-environments: junctions and mid-block locations. A model of pedestrians’ crossing behaviour along a trip is then developed, based on a hierarchical choice between junctions and mid-block locations and taking account of origin and destination, traffic characteristics and pedestrian facilities. Finally, a complete framework is produced for modelling pedestrians’ exposure in the light of their crossing behaviour. The feasibility of this approach is demonstrated on an artificial network and a first set of results is obtained from the validation of the models in observational studies.     

Risk factors in highway traffic accidents: a case control study

The objective of this study was to identify risk factors related to the driver, the vehicle and the environment, that are associated with motor vehicles accidents on highways. A case and control design was used where the case was: the driver of a motor vehicle who was involved in an accident while traveling on the Mexico-Cuernavaca highway; and the control was: the driver or a motor vehicle who, traveling on the highway, completed the trip without being involved in a traffic accident. Risk factors associated with the occurrence of a traffic accident (P > 0.05) were: age under 25 years, frequent travel, traveling to work, alcohol consumption, driving during the daylight, on a week-day, under adverse weather conditions and in the Mexico Cuernavaca direction of road. Risks adjusted by logistic regression were: age under 25 years odds ratio (OR) 3.01, confidence interval (CI) (95%) 1.46-6.18; work as a travel reason OR 1.74, CI 1.06 2.86; alcohol intake OR 4.70, Cl 1.62 13.6; driving under adverse weather conditions OR 5.70, CI 3.66-8.85; traveling on a week-day OR 1.84, CI 1.14-2.94; during daylight hours OR 4.23, CI 2.36-7.58 and in the Mexico Cuernavaca direction of road OR 2.69, CI 1.67 4.32. The identification of factors associated with the risk of being involved in a highway traffic accident allows us to propose primary prevention measures for this important public health problem.