Learning about injury severity from no-injury crashes: A random parameters with heterogeneity in means and variances approach

The traditional approach to injury-severity analyses does not allow in-depth understanding of no-injury crashes, as crash factors found to contribute to the various injury severities may have similar effects on the severity of vehicle damage even if no injury is recorded. Viewing no-injury crashes using the vehicle damage severities as sub-categories and bases for potential injuries can improve understanding of future injury crashes. To better understand the mechanism of no-injury crashes and the crash factors that contribute to the extent of vehicle damage beyond the single categorization of these crashes in injury severity analysis, this study presents a vehicle damage severity analysis for no-injury crashes. To compare the effects of crash contributing factors on crash outcomes, two injury severity models were also estimated. Random parameters multinomial logit models with heterogeneity in means and variances were developed to account for unobserved heterogeneity. Model estimation results revealed that several common factors (e.g., unsafe speed, distracted driving, driving under influence, vehicle age, and run-off-road) are correlated with both injury severity in injury crashes and vehicle damage severity in no-injury crashes. Therefore, the sub-categorization of no-injury crashes by vehicle damage severity can potentially improve estimates of injury severity considered in resource allocation decisions for traffic safety.

Correlated mixed logit modeling with heterogeneity in means for crash severity and surrogate measure with temporal instability

This study employs the correlated mixed logit models with heterogeneity in means by accounting for temporal instability to estimate both injury severity and vehicle damage. Two years of intersection crash data from Connecticut were analyzed based on driver characteristics, highway and traffic attributes, environmental variables, vehicle and crash types. These elements were used as independent variables to explore the contributing factors to crash outcome. The likelihood ratio test highlights that the temporal instability exists in both injury severity and vehicle damage models. The model estimation results illustrate that the means of some random parameters are different among crashes. The correlation coefficients of random parameters verify that these random parameters are not always independent, and their correlations should be considered and accounted for in crash severity estimation models. The investigation and comparison between injury severity models and vehicle damage models verify that the injury severity and vehicle damage are highly correlated, and the effects of contributing factors on vehicle damage are consistent with the results of injury severity models. This finding demonstrates that vehicle damage can be used as a potential surrogate measure to injury severity when suffering from a low sample of severe injury crashes in crash severity prediction models. It is anticipated that this study can shed light on selecting appropriate statistical models in crash severity estimation, identifying intersection crash contributing factors, and help develop effective countermeasures to improve intersection safety.

Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics - driver error, crash type, vehicle damage and injury severity - by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety. The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver's age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.

An evidence based method to calculate pedestrian crossing speeds in vehicle collisions (PCSC)

Pedestrian accident reconstruction is necessary to establish cause of death, i.e. establishing vehicle collision speed as well as circumstances leading to the pedestrian being impacted and determining culpability of those involved for subsequent court enquiry. Understanding the complexity of the pedestrian attitude during an accident investigation is necessary to ascertain the causes leading to the tragedy. A generic new method, named Pedestrian Crossing Speed Calculator (PCSC), based on vector algebra, is proposed to compute the pedestrian crossing speed at the moment of impact. PCSC uses vehicle damage and pedestrian anthropometric dimensions to establish a combination of head projection angles against the windscreen; this angle is then compared against the combined velocities angle created from the vehicle and the pedestrian crossing speed at the time of impact. This method has been verified using one accident fatality case in which the exact vehicle and pedestrian crossing speeds were known from Police forensic video analysis. PCSC was then applied on two other accident scenarios and correctly corroborated with the witness statements regarding the pedestrians crossing behaviours. The implications of PCSC could be significant once fully validated against further future accident data, as this method is reversible, allowing the computation of vehicle impact velocity from pedestrian crossing speed as well as verifying witness accounts.