Pedestrian at-fault crashes on rural and urban roadways in Alabama

The research described in this paper explored the factors contributing to the injury severity resulting from pedestrian at-fault crashes in rural and urban locations in Alabama incorporating the effects of randomness across the observations. Given the occurrence of a crash, random parameter logit models of injury severity (with possible outcomes of major, minor, and possible or no injury) for rural and urban locations were estimated. The estimated models identified statistically significant factors influencing the pedestrian injury severities. The results clearly indicated that there are differences between the influences of a variety of variables on the injury severities resulting from urban versus rural pedestrian at-fault accidents. The results showed that some variables were significant only in one location (urban or rural) but not in the other location. Also, estimation findings showed that several parameters could be modeled as random parameters indicating their varying influences on the injury severity. Based on the results obtained, this paper discusses the effects of different variables on pedestrian injury severities and their possible explanations. From planning and policy perspective, the results of this study justify the need for location specific pedestrian safety research and location specific carefully tailored pedestrian safety campaigns.     

Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways

In adverse driving conditions, such as inclement weather and/or complex terrain, trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the difference in driver-injury severity between SV and MV accidents by using mixed logit models. Injury severity from SV and MV accidents involving trucks on rural highways is modeled separately and their respective critical risk factors such as driver, vehicle, temporal, roadway, environmental and accident characteristics are evaluated. It is found that there exists substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies can be made. Estimation findings indicate that the snow road surface and light traffic indicators will be better modeled as random parameters in SV and MV models respectively. As a result, the complex interactions of various variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the injury severity of truck drivers from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future.     

Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis

This study explores the differences between urban and rural driver injuries (both passenger-vehicle and large-truck driver injuries) in accidents that involve large trucks (in excess of 10,000 pounds). Using 4 years of California accident data, and considering four driver-injury severity categories (no injury, complaint of pain, visible injury, and severe/fatal injury), a multinomial logit analysis of the data was conducted. Significant differences with respect to various risk factors including driver, vehicle, environmental, road geometry and traffic characteristics were found to exist between urban and rural models. For example, in rural accidents involving tractor-trailer combinations, the probability of drivers' injuries being severe/fatal increased about 26% relative to accidents involving single-unit trucks. In urban areas, this same probability increased nearly 700%. In accidents where alcohol or drug use was identified as being the primary cause of the accident, the probability of severe/fatal injury increased roughly 250% percent in rural areas and nearly 800% in urban areas. While many of the same variables were found to be significant in both rural and urban models (although often with quite different impact), there were 13 variables that significantly influenced driver-injury severity in rural but not urban areas, and 17 variables that significantly influenced driver-injury severity in urban but not rural areas. We speculate that the significant differences between rural and urban injury severities may be at least partially attributable to the different perceptual, cognitive and response demands placed on drivers in rural versus urban areas.     

Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways

Crashes occurring on rural two-lane highways are more likely to result in severe driver incapacitating injuries and fatalities. In this study, mixed logit models are developed to analyze driver injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes on rural two-lane highways in New Mexico from 2010 to 2011. A series of significant contributing factors in terms of driver behavior, weather conditions, environmental characteristics, roadway geometric features and traffic compositions, are identified and their impacts on injury severities are quantified for these two types of crashes, respectively. Elasticity analyses and transferability tests were conducted to better understand the models’ specification and generality. The research findings indicate that there are significant differences in causal attributes determining driver injury severities between SV and MV crashes. For example, more severe driver injuries and fatalities can be observed in MV crashes when motorcycles or trucks are involved. Dark lighting conditions and dusty weather conditions are found to significantly increase MV crash injury severities. However, SV crashes demonstrate different characteristics influencing driver injury severities. For example, the probability of having severe injury outcomes is higher when vans are identified in SV crashes. Drivers’ overtaking actions will significantly increase SV crash injury severities. Although some common attributes, such as alcohol impaired driving, are significant in both SV and MV crash severity models, their effects on different injury outcomes vary substantially. This study provides a better understanding of similarities and differences in significant contributing factors and their impacts on driver injury severities between SV and MV crashes on rural two-lane highways. It is also helpful to develop cost-effective solutions or appropriate injury prevention strategies for rural SV and MV crashes.     

Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.     

Highway accident severities and the mixed logit model: an exploratory empirical analysis

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.     

The relation between speed environment,age and injury outcome for bicyclists struck by a motorized vehicle – a comparison with pedestrians

This study analyzes (a) the relation between injury severities, the age of the bicyclist and the speed environment at accident locations (mean travel speed of the traffic flow involved in the accident) where a bicyclist was struck by a motorized vehicle and (b) how these relations differ from those for struck pedestrians. Accident data from Sweden for the years 2004–2008 was used to identify accident locations to analyze the relations between speed environment, age and injury outcome. Seventy-seven accident sites were used for field measurements and further analysis. The results show that both speed environment and age have considerable correlation with injury severity. There was a statistically significant relation between injury severity and the speed environment, and large proportion of the serious bicycle accidents occur at locations with speeds below 30 km/h. Also, the risk of serious injuries or fatalities seems to increase after the age of 45. To our knowledge this is the first study that uses the mean travel speed in this manner for analyzing injury severity of struck bicyclists.     

Statistical modelling of injury severity, with special reference to driver and front seat passenger in single-vehicle crashes

First, the statistical analysis of injury severity is introduced by considering the following topics: Interpretation of the recorded grades of injury severity (e.g. fatal, serious, slight, none) as divisions of a continuous scale. The possible presence of errors in recording injury severity. How this is used in the statistical analysis of injury severity data, including discussion of computing methods. Secondly, attention is turned to data in which the severities of injury to two people in the same crash is given. British accident data for 1969-72 has been processed to give a cross-tabulation of the severity of injury to the driver and to the front seat passenger in four types of single-vehicle accidents (overturning and nonoverturning, each in rural and in urban areas). Three complications with this data are that the number of non-injury accidents is unknown, that the cases where a passenger was present but uninjured could not be distinguished from those where there was no passenger, and that there is inconsistency in the positioning of the thresholds separating serious from slight injury, and slight from no injury. A positive correlation between the severities of injury to the two occupants is evident in the data. This is interpreted as being largely due to the speed of the crash, and a model is developed in which the two severities jointly have a bivariate normal distribution.     

Underreporting in traffic accident data, bias in parameters and the structure of injury severity models

Injury severities in traffic accidents are usually recorded on ordinal scales, and statistical models have been applied to investigate the effects of driver factors, vehicle characteristics, road geometrics and environmental conditions on injury severity. The unknown parameters in the models are in general estimated assuming random sampling from the population. Traffic accident data however suffer from underreporting effects, especially for lower injury severities. As a result, traffic accident data can be regarded as outcome-based samples with unknown population shares of the injury severities. An outcome-based sample is overrepresented by accidents of higher severities. As a result, outcome-based samples result in biased parameters which skew our inferences on the effect of key safety variables such as safety belt usage. The pseudo-likelihood function for the case with unknown population shares, which is the same as the conditional maximum likelihood for the case with known population shares, is applied in this study to examine the effects of severity underreporting on the parameter estimates. Sequential binary probit models and ordered-response probit models of injury severity are developed and compared in this study. Sequential binary probit models assume that the factors determining the severity change according to the level of the severity itself, while ordered-response probit models assume that the same factors correlate across all levels of severity. Estimation results suggest that the sequential binary probit models outperform the ordered-response probit models, and that the coefficient estimates for lap and shoulder belt use are biased if underreporting is not considered. Mean parameter bias due to underreporting can be significant. The findings show that underreporting on the outcome dimension may induce bias in inferences on a variety of factors. In particular, if underreporting is not accounted for, the marginal impacts of a variety of factors appear to be overestimated. Fixed objects and environmental conditions are overestimated in their impact on injury severity, as is the effect of separate lap and shoulder belt use. Combined lap and shoulder belt usage appears to be unaffected. The parameter bias is most pronounced when underreporting of possible injury accidents in addition to property damage only accidents is taken into account.     

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.     

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.     

Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes

In this study, a mixed logit model is developed to identify the heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. The random parameter of the variables in the mixed logit model, the heterogeneous mean, is elaborated by driver gender-based linear regression models. The model is estimated using crash data in New Mexico from 2010 to 2012. The percentage changes of factors’ predicted probabilities are calculated in order to better understand the model specifications. Female drivers are found more likely to experience severe or fatal injuries in rollover crashes than male drivers. However, the probability of male drivers being severely injured is higher than female drivers when the road surface is unpaved. Two other factors with fixed parameters are also found to significantly increase driver injury severities, including Wet and Alcohol Influenced. This study provides a better understanding of contributing factors influencing driver injury severities in rollover crashes as well as their heterogeneous impacts in terms of driver gender. Those results are also helpful to develop appropriate countermeasures and policies to reduce driver injury severities in single-vehicle rollover crashes.     

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.     

Occupant injury in rollover crashes – Contribution of planar impacts with objects and other vehicles

Planar impacts with objects and other vehicles may increase the risk and severity of injury in rollover crashes. The current study compares the frequency of injury measures (MAIS 2+, 3+, and 4+; fatal; AIS 2+ head and cervical spine; and AIS 3+ head and thorax) as well as vehicle type distribution (passenger car, SUV, van, and light truck), crash kinematics, and occupant demographics between single vehicle single event rollovers (SV Pure) and multiple event rollovers to determine which types of multiple event rollovers can be pooled with SV Pure to study rollover induced occupant injury. Four different types of multiple event rollovers were defined: single and multi-vehicle crashes for which the rollover is the most severe event (SV Prim and MV Prim) and single and multi-vehicle crashes for which the rollover is not the most severe event (SV Non-Prim and MV Non-Prim). Information from real world crashes was obtained from the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) for the period from 1995 through 2011. Belted, contained or partially ejected, adult occupants in vehicles that completed 1–16 lateral quarter turns were assigned to one of the five rollover categories. The results showed that the frequency of injury in non-primary rollovers (SV Non-Prim and MV Non-Prim) involving no more than one roof inversion is substantially greater than in SV Pure, but that this disparity diminishes for crashes involving multiple inversions. It can further be concluded that for a given number of roof inversions, the distribution of injuries and crash characteristics in SV Pure and SV Prim crashes are sufficiently similar for these categories to be considered collectively for purposes of understanding etiologies and developing strategies for prevention.     

Modeling occupant-level injury severity: An application to large-truck crashes

Most of the injury-severity analyses to date have focused primarily on modeling the most-severe injury of any crash, although a substantial fraction of crashes involve multiple vehicles and multiple persons. In this study, we present an extensive exploratory analysis that highlights that the highest injury severity is not necessarily the comprehensive indicator of the overall severity of any crash. Subsequently, we present a panel, hetroskedastic ordered-probit model to simultaneously analyze the injury severities of all persons involved in a crash. The models are estimated in the context of large-truck crashes. The results indicate strong effects of person-, driver-, vehicle-, and crash-characteristics on the injury severities of persons involved in large-truck crashes. For example, several driver behavior characteristics (such as use of illegal drugs, DUI, and inattention) were found to be statistically significant predictors of injury severity. The availability of airbags and the use of seat-belts are also found to be associated with less-severe injuries to car-drivers and car-passengers in the event of crashes with large trucks. Car drivers’ familiarity with the vehicle and the roadway are also important for both the car drivers and passengers. Finally, the models also indicate the strong presence of intra-vehicle correlations (effect of common vehicle-specific unobserved factors) among the injury propensities of all persons within a vehicle.     

Characterization of pedestrian accidents and an examination of infrastructure measures to improve pedestrian safety in Israel

The high share of pedestrian fatalities in Israel provided the impetus for this study which looked for infrastructure solutions to improve pedestrian safety. First, a detailed analysis of pedestrian accidents in 2006-2007, with an emphasis on the infrastructure characteristics involved, was performed; it found that 75% of the fatalities and 95% of the injuries occurred in urban areas, the majority of cases occurring on road sections (not at junctions). About 80% of the accidents took place when a pedestrian crossed the road, the majority of them at non-crosswalk locations or at non-signalized crosswalks. International comparisons showed that the characteristics of fatal pedestrian accidents in Israel were similar to the average pedestrian accident in Europe in terms of accident location, time, and the demographic characteristics of the victims. A typology of pedestrian fatalities in Israel was built for the years 2003-2006; it demonstrated a high share of accidents at these locations: in Jewish or mixed-population towns-not at pedestrian crossings on urban street sections, and both at pedestrian crossings and not at pedestrian crossings at urban junctions; in Arab towns; and on dual-carriageway rural roads. Second, based on a literature study, a summary of about 60 pedestrian-safety-related measures was developed. Third, to diagnose the infrastructure characteristics and deficiencies associated with pedestrian accidents, detailed field studies were carried out at 95 urban locations. A major finding revealed that more than 80% of the sites with a high concentration of pedestrian-vehicle accidents in Israel were situated on arterial multi-lane streets belonging to city centers, where on a micro-level there were no indications of major deficiencies in the basic design elements of most sites. Finally, cross-checking of the safety problems identified and the infrastructure solutions available provided lists of measures recommended for application at various types of sites. It was concluded that in order to generate a significant change in the state of pedestrian injury in Israel, a move from spot treatment to a systemic treatment of the problem is required. A systemic inquiry and the transformation of the urban road network should be performed in order to diminish the areas of vehicle-pedestrian conflicts and to significantly reduce vehicle speeds in areas of pedestrian presence and activity.     

Analyzing pedestrian crash injury severity at signalized and non-signalized locations

This study identifies and compares the significant factors affecting pedestrian crash injury severity at signalized and unsignalized intersections. The factors explored include geometric predictors (e.g., presence and type of crosswalk and presence of pedestrian refuge area), traffic predictors (e.g., annual average daily traffic (AADT), speed limit, and percentage of trucks), road user variables (e.g., pedestrian age and pedestrian maneuver before crash), environmental predictors (e.g., weather and lighting conditions), and vehicle-related predictors (e.g., vehicle type). The analysis was conducted using the mixed logit model, which allows the parameter estimates to randomly vary across the observations. The study used three years of pedestrian crash data from Florida. Police reports were reviewed in detail to have a better understanding of how each pedestrian crash occurred. Additionally, information that is unavailable in the crash records, such as at-fault road user and pedestrian maneuver, was collected. At signalized intersections, higher AADT, speed limit, and percentage of trucks; very old pedestrians; at-fault pedestrians; rainy weather; and dark lighting condition were associated with higher pedestrian severity risk. For example, a one-percent higher truck percentage increases the probability of severe injuries by 1.37%. A one-mile-per-hour higher speed limit increases the probability of severe injuries by 1.22%. At unsignalized intersections, pedestrian walking along roadway, middle and very old pedestrians, at-fault pedestrians, vans, dark lighting condition, and higher speed limit were associated with higher pedestrian severity risk. On the other hand, standard crosswalks were associated with 1.36% reduction in pedestrian severe injuries. Several countermeasures to reduce pedestrian injury severity are recommended.     

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.     

Analysis of injury severity of large truck crashes in work zones

Work zones are critical parts of the transportation infrastructure renewal process consisting of rehabilitation of roadways, maintenance, and utility work. Given the specific nature of a work zone (complex arrangements of traffic control devices and signs, narrow lanes, duration) a number of crashes occur with varying severities involving different vehicle sizes. In this paper we attempt to investigate the causal factors contributing to injury severity of large truck crashes in work zones. Considering the discrete nature of injury severity categories, a number of comparable econometric models were developed including multinomial logit (MNL), nested logit (NL), ordered logit (ORL), and generalized ordered logit (GORL) models. The MNL and NL models belong to the class of unordered discrete choice models and do not recognize the intrinsic ordinal nature of the injury severity data. The ORL and GORL models, on the other hand, belong to the ordered response framework that was specifically developed for handling ordinal dependent variables. Past literature did not find conclusive evidence in support of either framework. This study compared these alternate modeling frameworks for analyzing injury severity of crashes involving large trucks in work zones. The model estimation was undertaken by compiling a database of crashes that (1) involved large trucks and (2) occurred in work zones in the past 10 years in Minnesota. Empirical findings indicate that the GORL model provided superior data fit as compared to all the other models. Also, elasticity analysis was undertaken to quantify the magnitude of impact of different factors on work zone safety and the results of this analysis suggest the factors that increase the risk propensity of sustaining severe crashes in a work zone include crashes in the daytime, no control of access, higher speed limits, and crashes occurring on rural principal arterials.