A multivariate tobit analysis of highway accident-injury-severity rates

Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments.     

In-vehicle distraction and fatal accidents in England and Wales.

The paper describes the coding and analysis of a database of police fatal accident reports to investigate the extent to which in-vehicle distraction is a contributory factor in vehicle crashes. A particular focus has been the involvement of mobile telephones and entertainment systems. Analysis of accidents occurring over the period 1985-1995 shows that in-vehicle distraction is reported as a contributory factor in about 2% of fatal accidents (although this figure may be a conservative estimate). Specific examples of distraction attributed to entertainment systems and telephones have been identified. Electronic driver information systems are also of particular interest, but have not featured in the available data. Work is progressing, on an annual cycle, to obtain, code and analyse further data and this is expected to provide an invaluable source of information for accident researchers.     

Characteristics of low speed accidents with buses in public transport: part II

A number of accident characteristics of bus crashes are analyzed in relation to each other using data from 2237 accident involvements in the city of Uppsala (Sweden) during the years 1986-2000. The breakdown of accidents into sub-categories show, for example, that injury was common in intersection accidents, that bus stops present large risk for shunts and side contacts, while single vehicle accidents were seldom preceded by the loss of control or a skid. The treatment of accident data is discussed in terms of methodology, statistics and data reduction strategies.     

Factors correlated with traffic accidents as a basis for evaluating Advanced Driver Assistance Systems

This study aims to identify factors which influence and cause errors in traffic accidents and to use these as a basis for information to guide the application and design of driver assistance systems. A total of 474 accidents were examined in depth for this study by means of a psychological survey, data from accident reports, and technical reconstruction information. An error analysis was subsequently carried out, taking into account the driver, environment, and vehicle sub-systems. Results showed that all accidents were influenced by errors as a consequence of distraction and reduced activity. For crossroad accidents, there were further errors resulting from sight obstruction, masked stimuli, focus errors, and law infringements. Lane departure crashes were additionally caused by errors as a result of masked stimuli, law infringements, expectation errors as well as objective and action slips, while same direction accidents occurred additionally because of focus errors, expectation errors, and objective and action slips. Most accidents were influenced by multiple factors. There is a safety potential for Advanced Driver Assistance Systems (ADAS), which support the driver in information assimilation and help to avoid distraction and reduced activity. The design of the ADAS is dependent on the specific influencing factors of the accident type.     

A crash-prediction model for road tunnels

Considerable research has been carried out into open roads to establish relationships between crashes and traffic flow, geometry of infrastructure and environmental factors, whereas crash-prediction models for road tunnels, have rarely been investigated. In addition different results have been sometimes obtained regarding the effects of traffic and geometry on crashes in road tunnels. However, most research has focused on tunnels where traffic and geometric conditions, as well as driving behaviour, differ from those in Italy. Thus, in this paper crash prediction-models that had not yet been proposed for Italian road tunnels have been developed. For the purpose, a 4-year monitoring period extending from 2006 to 2009 was considered. The tunnels investigated are single-tube ones with unidirectional traffic. The Bivariate Negative Binomial regression model, jointly applied to non-severe crashes (accidents involving material-damage only) and severe crashes (fatal and injury accidents only), was used to model the frequency of accident occurrence. The year effect on severe crashes was also analyzed by the Random Effects Binomial regression model and the Negative Multinomial regression model. Regression parameters were estimated by the Maximum Likelihood Method. The Cumulative Residual Method was used to test the adequacy of the regression model through the range of annual average daily traffic per lane. The candidate set of variables was: tunnel length (L), annual average daily traffic per lane (AADT_L), percentage of trucks (%Tr), number of lanes (N_L), and the presence of a sidewalk. Both for non-severe crashes and severe crashes, prediction-models showed that significant variables are: L, AADT_L, %Tr, and N_L. A significant year effect consisting in a systematic reduction of severe crashes over time was also detected. The analysis developed in this paper appears to be useful for many applications such as the estimation of accident reductions due to improvement in existing tunnels and/or to modifications of traffic control systems, as well as for the prediction of accidents when different tunnel design options are compared.     

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.     

一起TNT碱性废水沉淀池火灾事故分析

某化工厂TNT碱性废水沉淀池在清理过程中发生了火灾事故,在现场调查的过程中,从附着在沉淀池上的可燃物成分入手,详细分析了造成此次火灾事故的主要原因,并在假定附着在池壁及立柱上的红棕色物质为纯TNT和事故中TNT为等容、绝热燃烧的前提下,计算出了事故中TNT燃烧的最高温度及最大压力.对类似工程中的防火防爆工作提供参考.     

Accident prediction model for railway-highway interfaces

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.     

Quasi-induced exposure: The choice of exposure metrics

The quasi-induced exposure method is widely used to estimate exposure and risks of different groups of drivers and vehicles. Essentially, this method assumes that non-at-fault or passive parties in two-vehicle collisions represent a random sample of the populations on the road. Most previous works have used the whole sample of collisions to estimate exposure.There has been some concern about possible biases in quasi-induced estimates. In this paper, we argue that (1) biases are mainly due to differences in accident avoidance abilities, speeds and injury risks, and (2) because the influence of these three factors on the probability of being non-at-fault is not the same for every crash type, differences may arise among non-at-fault populations, in which case some crash types would provide a more accurate estimate of exposure than others.We explore the direction of biases due to speed, accident avoidance ability and injury risk in four accident types: accidents between vehicles travelling on different lanes in two-way, two-lane undivided roads; accidents between vehicles travelling on different lanes on multilane roads; intersection accidents; and accidents between vehicles travelling on the same lane. Our analysis shows that more research would be needed concerning the effect of speed on head-on crashes on undivided roads, and crashes on multilane roads.     

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.     

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Accident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.     

Modeling fault among accident—Involved pedestrians and motorists in Hawaii

Using a comprehensive database of police-reported accidents in Hawaii, we describe the nature of pedestrian accidents over the period 2002–2005. Approximately 36% of the accidents occur in residential areas, while another 34% occur in business areas. Only 41.7% of the pedestrian accidents occur at intersections. More pedestrian crashes occur at non-intersection locations—including midblock locations, driveways, parking lots, and other off roadway locations. Approximately 38.2% of the crashes occur at crosswalk locations, while proportionately more (61.8%) of the pedestrian accidents occur at non-crosswalk locations. Using this database the human, temporal, roadway, and environmental factors associated with being “at-fault” for both pedestrians and drivers are also examined. Using techniques of logistic regression, several different explanatory models are constructed, to identify the factors associated with crashes producing fatalities and serious injuries. Finally, two pedestrian models (drunk males and young boys) and one driver model (male commuters) are developed to provide further understanding of pedestrian accident causation. Drunk male pedestrians who were jaywalking were in excess of 10× more likely than other groups to be at-fault in pedestrian accidents. Young boys in residential areas were also more likely to be at-fault. Male commuters in business areas in the morning were also found to have higher odds of being classified at-fault when involved in pedestrian accidents. The results of this study indicate that there should be a combination of enforcement and educational programs implemented for both the pedestrian and drivers to show those at-fault the consequences of their actions, and to reduce the overall number of accidents.     

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.     

Hit-and-run crashes in urban river-crossing road tunnels

Hit-and-run crashes are a relatively infrequent but severe offense worldwide because the identification and emergency rescue of victims is delayed, which increases the injury severities and the mortality rate. However, no studies have been conducted on hit-and-run crashes in urban river-crossing road tunnels (URCRTs), which can greatly threaten the safety of motorists driving in the tunnels. This study, which employs a dataset of vehicle crashes that happened in thirteen urban road tunnels traversing the Huangpu River, established a binary logistic regression model to identify thirteen factors that contribute to escaping after crashes in Shanghai related to the offending drivers, the vehicular and environmental conditions, the tunnel characteristics and crash information. Among the thirty-five variables considered, this study found that a perpetrator's tendency to leave the crash scene without reporting an accident was higher at night, in the tunnel exit, near to or in short tunnels, when a two-wheeled vehicle or heavy goods vehicle (HGV) was involved and when alcohol was involved. While a perpetrator was more likely to remain on the scene in the tunnel entrance, on a rainy day, in a rear end collision, when a bus was involved, in a single vehicle or a multi-vehicle accident. Based on these findings, several countermeasures for better supervision and hit-and-run prevention are proposed.     

A model for evaluating the effectiveness of motor vehicle inspection programs

A statistical model for the evaluation of the effectiveness of motor vehicle inspection programs in reducing highway crashes is presented. The model is based on the assumption that the waiting time between highway crashes follows an exponential distribution. Since highway crashes are relatively rare events, it is assumed that the length of the study period is such that censoring occurs. Under these assumptions, maximum likelihood estimates of the mean waiting time θ until a crash for the non-inspected (inspected) vehicles is obtained and the corresponding test statistic is derived. As mechanically-caused accidents are but a small part of the overall accident picture and since inspection should only affect this portion, sample size requirements are investigated for various combinations of θ, Δ (increase in average time until a crash due to the effect of inspection), L (length of study period), and = β (probability of Type I error equalling probability of Type II error). For reasonable Δ, the sample required is indeed sizable.     

Are speed enforcement cameras more effective than other speed management measures? An evaluation of the relationship between speed and accident reductions

In this paper, models are developed which enable a prediction of how the impact of speed management schemes on accidents varies both with speed changes and with site and scheme characteristics. It was found that, the impact of schemes with vertical deflections is independent of the change in mean speed: an accident reduction of 44% is predicted by the model irrespective of the impact on speed. For cameras and other types of engineering schemes, a simple relationship between the change in mean speed and the consequent change in accidents is available. For the range of mean speeds typically found on 30 mph roads, the percentage accident reduction per 1 mph speed reduction is around 4% for cameras and 7-8% for schemes with horizontal features. While larger percentage accident reductions are achieved per 1 mph speed reduction on lower speed roads, larger speed reductions and larger overall percentage accident reductions are obtained on roads with higher before mean speeds. It is possible to predict both changes in speeds and accidents before treatment using the models derived from this study and these models confirm that schemes with vertical deflections are most effective in reducing both speeds and accidents.     

Bayesian-network-based safety risk assessment for steel construction projects

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites.     

The role of alcohol in Thailand motorcycle crashes

In order to identify motorcycle accident cause factors and countermeasures in Thailand, a large prospective study was undertaken. Researchers conducted on-scene, in-depth investigation and reconstruction of 969 collisions involving 1082 motorcycle riders. Accidents were randomly sampled and included all levels of injury severity. Alcohol proved to be the most outstanding cause factor, with 393 drinking riders in crashes. Alcohol accidents were distinctly different from non-alcohol crashes. Alcohol accidents were more frequent on weekends and particularly at night, usually when the rider was on his way home. Drinking riders were more likely to lose control of the motorcycle, usually by running off the road. They were more likely to be in a single vehicle accident, to violate traffic control signals, and to be in non-intersection collisions. Males were far more likely to drink and ride than females. Drinking riders were far more likely to be inattentive to the driving task just before they crashed, and to be the primary or sole cause of the accident. One-fourth of all riders did not go to the hospital, and another 42% needed only treatment in the emergency room. Drinking riders were more likely to be hospitalized and far more likely to be killed. The higher hospitalization and fatality rates of drinking riders resulted from the kinds of accidents in which they were involved, not from the minimal differences in speeds and helmet use. Problems with balance and coordination were about equally rare among drinking and non-drinking riders. Inattention was a far greater contributing factor.     

Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis

In the US, single-vehicle run-off-roadway accidents result in a million highway crashes with roadside features every year and account for approximately one third of all highway fatalities. Despite the number and severity of run-off-roadway accidents, quantification of the effect of possible countermeasures has been surprisingly limited due to the absence of data (particularly data on roadside features) needed to rigorously analyze factors affecting the frequency and severity of run-off-roadway accidents. This study provides some initial insight into this important problem by combining a number of databases, including a detailed database on roadside features, to analyze run-off-roadway accidents on a 96.6-km section of highway in Washington State. Using zero-inflated count models and nested logit models, statistical models of accident frequency and severity are estimated and the findings isolate a wide range of factors that significantly influence the frequency and severity of run-off-roadway accidents. The marginal effects of these factors are computed to provide an indication on the effectiveness of potential countermeasures. The findings show significant promise in applying new methodological approaches to run-off-roadway accident analysis.     

A mixed logit analysis of motorists’ right-of-way violation in motorcycle accidents at priority T-junctions

Research suggested that motorists’ right-of-way (ROW) violation in automobile-motorcycle gap-acceptance accidents at priority (i.e., stop-/yield-controlled) T-intersections has been a safety concern to motorcyclists. This study examines the characteristics of automobile-motorcycle gap-acceptance accidents that occurred at such locations. British Stats19 accident injury database during 1991-2005 are examined in detail. Automobile-motorcycle gap-acceptance accidents are classified into three crash scenarios: approach-turn, angle crossing, and angle merging crashes. Mixed (random parameters) logit models are estimated to investigate the contributory factors to motorists’ ROW violation in these three crash types. Crash features are also compared among gap-acceptance accidents and other crash scenarios. The methodological approach adopted allows for the individuals within the observations to have different parameter estimates as opposed to a single parameter representing all observations (i.e., accounts for unobserved heterogeneity potentially relating to roadway/environmental characteristics, and motorist behaviours). It was found that motorcycles’ ROW was more likely to be violated on non-built-up roads, and in diminished light conditions, with non-uniform effects across the observations. Elderly/female motorists appeared to be over-represented in gap-acceptance crashes. Implications of the findings are discussed.