A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes

Leaving the scene of a crash without reporting it is an offence in most countries and many studies have been devoted to improving ways to identify hit-and-run vehicles and the drivers involved. However, relatively few studies have been conducted on identifying factors that contribute to the decision to run after the crash. This study identifies the factors that are associated with the likelihood of hit-and-run crashes including driver characteristics, vehicle types, crash characteristics, roadway features and environmental characteristics. Using a logistic regression model to delineate hit-and-run crashes from nonhit-and-run crashes, this study found that drivers were more likely to run when crashes occurred at night, on a bridge and flyover, bend, straight road and near shop houses; involved two vehicles, two-wheel vehicles and vehicles from neighboring countries; and when the driver was a male, minority, and aged between 45 and 69. On the other hand, collisions involving right turn and U-turn maneuvers, and occurring on undivided roads were less likely to be hit-and-run crashes.     

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.     

Risk and type of crash among young drivers by rurality of residence: Findings from the DRIVE Study

Background

Most previous literature on urban/rural differences in road crashes has a primary focus on severe injuries or deaths, which may be largely explained by variations of medical resources. Little has been reported on police-reported crashes by geographical location, or crash type and severity, especially among young drivers.

Methods

DRIVE is a prospective cohort study of 20,822 drivers aged 17-24 in NSW, Australia. Information on risk factors was collected via online questionnaire and subsequently linked to police-reported crashes. Poisson regression was used to analyse risk of various crash types by three levels of rurality of residence: urban, regional (country towns and surrounds) and rural.

Results

Compared to urban drivers, risk of crash decreased with increasing rurality (regional adjusted RR: 0.7, 95% CI 0.6-0.9; rural adjusted RR: 0.5, 95% CI 0.3-0.7). Among those who crashed, risk of injurious crash did not differ by geographic location; however, regional and rural drivers had significantly higher risk of a single versus multiple vehicle crash (regional adjusted RR 1.8, 95% CI 1.3-2.5; rural adjusted RR: 2.0, 95% CI 1.1-3.6), which was explained by speeding involvement and road alignment at the time or site of crash.

Conclusions

Although young urban drivers have a higher crash risk overall, rural and regional residents have increased risk of a single vehicle crash. Interventions to reduce single vehicle crashes should aim to address key issues affecting such crashes, including speeding and specific aspects of road geometry.     

Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.     

Estimation of rear-end vehicle crash frequencies in urban road tunnels

According to The Handbook of Tunnel Fire Safety, over 90% (55 out of 61 cases) of fires in road tunnels are caused by vehicle crashes (especially rear-end crashes). It is thus important to develop a proper methodology that is able to estimate the rear-end vehicle crash frequency in road tunnels. In this paper, we first analyze the time to collision (TTC) data collected from two road tunnels of Singapore and conclude that Inverse Gaussian distribution is the best-fitted distribution to the TTC data. An Inverse Gaussian regression model is hence used to establish the relationship between the TTC and its contributing factors. We then proceed to introduce a new concept of exposure to traffic conflicts as the mean sojourn time in a given time period that vehicles are exposed to dangerous scenarios, namely, the TTC is lower than a predetermined threshold value. We further establish the relationship between the proposed exposure to traffic conflicts and crash count by using negative binomial regression models. Based on the limited data samples used in this study, the negative binomial regression models perform well although a further study using more data is needed.     

The relative contribution of system failures and extreme behaviour in South Australian crashes

Within the road system, there are compliant road users who may make an error that leads to a crash, resulting in a ‘system failure’, and there are also road users who deliberately take risks and display dangerous or ‘extreme’ behaviours that lead to a crash. Crashes resulting from system failures can be addressed through improvements to road system design more readily than crashes resulting from extreme behaviours. The classification of crash causation in terms of system failures or extreme behaviour is important for determining the extent to which a Safe System approach (i.e. improvements to road system design to serve compliant road users) is capable of reducing the number of crashes. This study examined the relative contribution of system failures and extreme behaviour in South Australian crashes as identified from information in Coroner’s investigation files and in-depth crash investigations conducted by the Centre for Automotive Safety Research (CASR). The analysis of 189 fatal crashes, 272 non-fatal metropolitan injury crashes and 181 non-fatal rural crashes indicated that very few non-fatal crashes (3% metropolitan, 9% rural) involved extreme behaviour by road users and, even in fatal crashes, the majority (54%) were the result of system failures. Fatal crashes resulting from system failures were more likely than those resulting from extreme behaviour to occur during the day, on weekdays, in rural areas and on roads with high speed limits. Findings from the current study suggest that improvements to the road transport system (i.e. forgiving road infrastructure, appropriate speed limits, and safe vehicle design) can be expected to be much more effective in reducing crashes than concentrating on preventing extreme behaviours. Such a strategy could reduce the incidence and severity of a large proportion of crashes in South Australia.     

Pedestrian- and driver-related factors associated with the risk of causing collisions involving pedestrians in Spain

This study aimed to quantify the association between pedestrian- and driver-related factors and the risk of causing road crashes involving pedestrians in urban areas in Spain between 1993 and 2011. From the nationwide police-based registry of road crashes with victims in Spain, we analyzed all 63,205 pairs of pedestrians and drivers involved in crashes in urban areas in which only the pedestrian or only the driver was at fault. Logistic regression models were used to obtain adjusted odds ratios to assess the strength of association between each individual-related variable and the pedestrian’s odds of being at fault for the crash (and conversely, the driver’s odds of not being at fault).The subgroups of road users at high risk of causing a road crash with a pedestrian in urban areas were young and male pedestrians, pedestrians with psychophysical conditions or health problems, the youngest and the oldest drivers, and drivers with markers of high-risk behaviors (alcohol use, nonuse of safety devices, and driving without a valid license). These subgroups should be targeted by preventive strategies intended to decrease the rate of urban road crashes involving pedestrians in Spain.     

Attributions of responsibility for motor vehicle crashes

A sample of 321 motor vehicle crash survivors completed a survey in which they provided attribution ratings of the extent to which they were responsible for their crashes, other people (drivers) were responsible, or road/weather conditions were responsible. The attribution ratings were consistent with the predictions of defensive attribution theory (DAT; [Walster, E., 1966. Assignment of responsibility for an accident. J. Pers. Soc. Psychol. 3, 73-79]) in that people who experienced crashes of greater severity (necessitating medical treatment for injuries) attributed greater responsibility to other drivers than to self or to weather/road conditions. People who were in crashes of lesser severity attributed approximately the same amount of responsibility to themselves as they did to others. An actor-observer effect also appeared in survivors' attribution ratings in that self-acceptance of responsibility for the crash was positively correlated with attributions to the situation (road/weather conditions) whereas such attributions to the situation were negatively correlated with attributions of responsibility to other drivers. Consistent with results of prior research, survivors who assigned crash responsibility to other drivers reported increased levels of driving and riding avoidance compared to people who accepted responsibility for their crashes.     

The effect of road tunnel environment on car following behaviour

In order to overcome urban space constraints, underground road systems are becoming popular options for cities. Existing literature suggests that accident rates in road tunnels are lower than those in open roads. However, there is a lack of understanding in how the road tunnel environment affects inter-vehicle interactions. In this study, car following data are obtained from traffic video footages of open and tunnel expressways in Singapore. A total of 15,325 car following headways (with car as the follower) are analysed and significant factors affecting headways are found to be speed, and lane. Significant effect of leading vehicle type is only found for tunnel expressway. Headways are generally longer in the tunnel environment. Assessment of collision time measures and safety margins also reveal safer car following behaviour and lower rear–end collision risks in the tunnel expressway. The results are discussed from a behavioural perspective. Overall, the findings show that road tunnels are superior in terms of safety but at reduced traffic capacity .     

Risk factors for severe injury in cyclists involved in traffic crashes in Victoria,Australia

This study examines the impact of cyclist, road and crash characteristics on the injury severity of cyclists involved in traffic crashes reported to the police in Victoria, Australia between 2004 and 2008. Logistic regression analysis was carried out to identify predictors of severe injury (serious injury and fatality) in cyclist crashes reported to the police. There were 6432 cyclist crashes reported to the police in Victoria between 2004 and 2008 with 2181 (33.9%) resulting in severe injury of the cyclist involved. The multivariate analysis found that factors that increase the risk of severe injury in cyclists involved in traffic crashes were age (50 years and older), not wearing a helmet, riding in the dark on unlit roads, riding on roads zoned 70 km/h or above, on curved sections of the road, in rural locations and being involved in head-on collisions as well as off path crashes, which include losing control of vehicle, and on path crashes which include striking the door of a parked vehicle. While this study did not test effectiveness of preventative measures, policy makers should consider implementation of programs that address these risk factors including helmet programs and environmental modifications such as speed reduction on roads that are frequented by cyclists.     

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.     

Road crashes involving animals in Australia

Each year in Australia many thousands of collisions occur between motor vehicles and animals, resulting in considerable vehicle repair costs, injury to persons, and loss of animal life. This paper reviews animal-related road crashes in Australia and presents data from the in-depth Rural and Remote Road Safety Study in North Queensland for serious casualties (n = 33) resulting from direct impact with an animal or swerving to avoid an animal on public roads. These crash types accounted for 5.5% of all eligible on-road serious casualties in the study and, hence, are considered to be an important issue that requires particular attention within rural and remote areas. Kangaroos and wallabies were the predominant species involved in these crashes (44.8%). Consistent with international studies, night-time travel was found to be a significant risk factor when comparing animal-related crashes to other serious injury crashes in the study. There were also a significantly higher proportion of motorcyclists (51.7%) than other vehicle occupants involved in animal-related serious crashes compared to all other serious injury crashes. Data matching to official Government records found underreporting of animal-related crashes to be an issue of concern. These findings are discussed in terms of countermeasures suitable for the Australian context and the need for consistent crash reporting across jurisdictions.     

Vehicle mass and injury risk in two-car crashes: A novel methodology

This paper introduces a novel methodology based on disaggregate analysis of two-car crash data to estimate the partial effects of mass, through the velocity change, on absolute driver injury risk in each of the vehicles involved in the crash when absolute injury risk is defined as the probability of injury when the vehicle is involved in a two-car crash. The novel aspect of the introduced methodology is in providing a solution to the issue of lack of data on the speed of vehicles prior to the crash, which is required to calculate the velocity change, as well as a solution to the issue of lack of information on non-injury two-car crashes in national accident data. These issues have often led to focussing on relative measures of injury risk that are not independent of risk in the colliding cars. Furthermore, the introduced methodology is used to investigate whether there is any effect of vehicle size above and beyond that of mass ratio, and whether there are any effects associated with the gender and age of the drivers. The methodology was used to analyse two-car crashes to investigate the partial effects of vehicle mass and size on absolute driver injury risk. The results confirmed that in a two-car collision, vehicle mass has a protective effect on its own driver injury risk and an aggressive effect on the driver injury risk of the colliding vehicle. The results also confirmed that there is a protective effect of vehicle size above and beyond that of vehicle mass for frontal and front to side collisions.     

Are SUVs dangerous vehicles?

This study was a population cohort study of all licensed passenger vehicles in New Zealand in the years 2005–2006. The objective of the study was to evaluate the effect on road safety of sports utility vehicles (SUVs) compared to other passenger vehicle types. Statistical models were fitted to the population of 2,996,000 vehicles of which 17,245 were involved in an injury crash. Controlling for distance driven, vehicle and owner characteristics, SUVs were found to be relatively safe vehicles in terms of injury crash involvement and in terms of the injury rate of their own occupants or other road users into which they crashed. Current research on SUV safety clearly shows them to be a road safety concern, but only once a collision occurs. The present study shows that SUVs in New Zealand have relatively few collisions compared to other passenger vehicle types, allowing for factors such as distance driven, some allowance for the type of driving exposure (via the owners’ addresses) and for owner age and gender. Overall, the vehicle type implicated most frequently in injury crashes and involving the highest rate of road injuries was sports cars, causing clearly the most harm per licensed vehicle. Instead of concerning themselves primarily with SUVs, the focus of road safety agencies should be on the relatively high crash risk of sports cars, which are clearly a road safety concern. Their high crash involvement rate and injury rate is likely to be largely due to the way they are driven rather than to inherent characteristics of the vehicles themselves.     

The impact of tunnel design and lighting on the performance of attentive and visually distracted drivers

The crash risk in tunnels is lower than on the open road network, but the consequences of a crash are often severe. Proper tunnel design is one measure to reduce the likelihood of crashes, and the objective of this work is to investigate how driving performance is influenced by design factors, and whether there is an interaction with secondary task load. Twenty-eight drivers participated in the simulator study. A full factorial within subject design was used to investigate the tunnel wall colour (dark or light-coloured walls), illumination (three different levels) and task load (with or without a visual secondary task). The results show that tunnel design and illumination have some influence on the drivers’ behaviour, but visual attention given to the driving task is the most crucial factor, giving rise to significant changes in both driving behaviour and visual behaviour. The results also indicate that light-coloured tunnel walls are more important than strong illumination to keep the drivers’ visual attention focused forward.     

A simple method for identifying and correcting crash problems on urban arterial streets

Urban arterials by their nature carry heavy traffic volumes and generate large numbers of motor vehicle crashes. The present study involved review of police crash reports to identify precrash events and driver actions for a sample of crashes on urban arterials and describes a method for reducing such crashes based on analyses of collision patterns and identification of locations with excessive numbers of crashes of a particular type. Police-reported crash data were obtained for three urban arterials in the Washington, DC metropolitan area. A total of 2013 crash reports were analyzed. Seven crash types accounted for nearly 90% of these reports. On each arterial studied, several locations with excessive numbers of crashes of a particular type were identified, and corresponding engineering countermeasures were recommended. Differences between the approach employed in this study and traditional blackspot analyses are discussed.     

An empirical analysis of farm vehicle crash injury severities on Iowa's public road system

Farm vehicle crashes are a major safety concern for farmers as well as all other users of the public road system in agricultural states. Using data on farm vehicle crashes that occurred on Iowa's public roads between 2004 and 2006, we estimate a multinomial logit model to identify crash-, farm vehicle-, and driver-specific factors that determine farm vehicle crash injury severity outcomes. Estimation findings indicate that there are crash patterns (rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median) and conditions (obstructed vision and crash in rural area; dry road, dark lighting, speed limit 55 mph or higher, and harvesting season), as well as farm vehicle and driver-contributing characteristics (old farm vehicle, young farm vehicle driver), where targeted intervention can help reduce the severity of crash outcomes. Determining these contributing factors and their effect is the first step to identifying countermeasures and safety strategies in a bid to improve transportation safety for all users on the public road system in Iowa as well as other agricultural states.     

An examination of the environmental,driver and vehicle factors associated with the serious and fatal crashes of older rural drivers

Motor vehicle crashes involving rural drivers aged 75 years and over are more than twice as likely to result in a serious or fatal injury as those involving their urban counterparts. The current study examined some of the reasons for this using a database of police-reported crashes (2004–2008) to identify the environmental (lighting, road and weather conditions, road layout, road surface, speed limit), driver (driver error, crash type), and vehicle (vehicle age) factors that are associated with the crashes of older rural drivers. It also determined whether these same factors are associated with an increased likelihood of serious or fatal injury in younger drivers for whom frailty does not contribute to the resulting injury severity. A number of environmental (i.e., undivided, unsealed, curved and inclined roads, and areas with a speed limit of 100 km/h or greater) and driver (i.e., collision with a fixed object and rolling over) factors were more frequent in the crashes of older rural drivers and additionally associated with increased injury severity in younger drivers. Moreover, when these environmental factors were entered into a logistic regression model to predict whether older drivers who were involved in crashes did or did not sustain a serious or fatal injury, it was found that each factor independently increased the likelihood of a serious or fatal injury. Changes, such as the provision of divided and sealed roads, greater protection from fixed roadside objects, and reduced speed limits, appear to be indicated in order to improve the safety of the rural driving environment for drivers of all ages. Additionally, older rural drivers should be encouraged to reduce their exposure to these risky circumstances.     

Toll road crashes of commercial and passenger motor vehicles

Revenue-collection data from toll roads allow for accurate estimates of miles driven by vehicle type and, when combined with crash data, valid estimates of crash involvements per mile driven. Data on vehicle-miles traveled and collisions were obtained from toll road authorities in Florida. Kansas, and New York. In addition, state crash files and published vehicle-miles of travel were obtained for toll roads in Illinois. Indiana, Ohio, and Pennsylvania. Large commercial motor vehicles were significantly underinvolved in single-vehicle crashes on all state toll roads. In five states, commercial motor vehicles were significantly overinvolved in multiple-vehicle crashes relative to passenger vehicles; the exceptions were Kansas, where they had significantly lower multiple-vehicle involvement rates, and Indiana. where there were no significant differences in multiple-vehicle involvements by vehicle type. The risk of commercial motor vehicle involvement in multiple-vehicle crashes resulting in deaths or serious injuries was double that of passenger vehicles in the two states (Ohio and Pennsylvania) that identified serious injuries. Whether crash rates, on toll roads of commercial motor vehicles are higher or lower than those of passenger vehicles appears to depend on the type of crash, specific toll road. and traffic density.     

Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention.