Driver Injury Severity In Single-vehicle Crashes Research Articles

An empirical analysis of how asleep/fatigued driving-injury severities have changed over time

Asleep/fatigued driving has proven to be a serious and persistent highway-safety problem. This study investigates aspects of this problem by studying the temporal changes in driver-injury severities in single-vehicle crashes that involve asleep/fatigued driving. To do this, random parameters logit models with unobserved heterogeneity in means and variances were estimated to compare injury-severities in asleep/fatigued crashes in Florida in 2014 and 2019. The estimated models are based on available police-reported crash data that include a wide variety of factors related to the spatial, temporal, and weather characteristics as well as vehicle characteristics, traffic information, harmful events, roadway attributes, and driver characteristics. The model estimates show that there were many statistically significant factors determining driver-injury severities resulting from asleep/fatigued driving, and that the effect of these factors on driver-injury severities has changed significantly over time, with many explanatory variables producing temporally shifting marginal effects. While asleep/fatigued driving crashes remain a serious safety concern, the empirical findings indicate (using model prediction simulations) that the resulting injury severities in crashes involving asleep/fatigued driving have declined between 2014 and 2019, likely reflecting the effectiveness of safety campaigns and ongoing improvements in vehicle safety technologies and highway safety features.

Temporal stability of factors influencing driver-injury severities in single-vehicle crashes: A correlated random parameters with heterogeneity in means and variances approach

Undoubtedly, single-vehicle crashes remain a major concern for roadway users and highway administrators, especially in low- and middle-income developing countries, where accident death rates remain extremely high. This study investigates the temporal instability of contributing factors of driver-injury severities in single-vehicle crashes using data in Thailand, a developing country, from 2011 to 2017. Uncorrelated and correlated random parameters models with possible heterogeneity in means and variances were estimated for individual year model using two levels of driver-injury severities, namely, no/minor injury and severe/fatal injury. The models considered a wide range of factors, such as driver, roadway, vehicle, crash, environmental and temporal, and spatial characteristics. A series of likelihood ratio test and the marginal effect of significant factors were computed to explore the temporal stability of the year models and to investigate the temporal instability of the effect of each parameter estimate on the probability of driver-injury severities within given time periods. The result indicates that a substantial temporal instability exists in the model specifications and estimated parameters (temporally unstable factor included male driver, driving using exceeding speed limit, crashes on asphalt pavement, crashes on weekends, and crashes on weekend during nighttime with present of road lighting) across the time periods under study (despite being insignificant in particular year models, some factors were stable but marginal effects varied across time). The findings may be used to assist and guide decision makers in policy generation for plans to mitigate driver-injury severities. Despite the unclear source of temporal instability, the findings emphasize the importance of the temporal instability of the factors that influence the outcomes of driver-injury severities. Alternatively, ignoring temporal instability in studies on crash severity may lead to high levels of bias and inaccurate conclusions. With regard to methodologies, both random parameters with heterogeneity in means and variances and correlated random parameters with heterogeneity in means approaches are promising methods with ability to offer another layer of insight into unobserved heterogeneity in injury severity research.

The effect of passengers on driver-injury severities in single-vehicle crashes: A random parameters heterogeneity-in-means approach

This paper seeks to investigate the effects of passengers on driver-injury severities. Using single-vehicle crashes, a random parameters logit model with heterogeneity in parameter means is estimated to explore the differences in driver-injury severities in three distinct subgroups; vehicles with one occupant (driver-only), vehicles with two occupants (driver and a passenger), and vehicles with three occupants (driver and two passengers). In addition to considering age, gender and the presence of the passenger(s), a wide range of variables that potentially affect driver-injury severity were considered, including weather conditions, roadway characteristics, vehicle characteristics and driver attributes. Estimation results show that both the age and the gender of the passenger(s) significantly affected driver-injury severities. The findings of this research point toward the need to further study the potentially complex interactions between drivers and passengers.

The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: Some empirical evidence

This study explores the temporal stability of factors affecting driver-injury severities in single-vehicle crashes. Using data for single-vehicle crashes in Chicago, Illinois from a nine-year period from January 1, 2004 to December 31, 2012, separate annual models of driver-injury severities (with possible outcomes of severe injury, minor injury, and no injury) were estimated using a mixed logit model to capture potential unobserved heterogeneity. Likelihood ratio tests were conducted to examine the overall stability of model estimates across time periods and marginal effects of each explanatory variable were also considered to investigate the temporal stability of the effect of individual parameter estimates on injury-severity probabilities. A wide range of variables potentially affecting injury severities was considered including driver-contributing factors, location and time of day, crash-specific factors, driver attributes, roadway characteristics, environmental conditions, and vehicle characteristics. The results indicated that, although data from different years share some common features, the model specifications and estimated parameters are not temporally stable. In addition, complex temporal stability behaviors were observed for individual parameter estimates such as driver gender, apparent physical condition of driver, type of vehicle, vehicle occupancy, road surface, weather, and light conditions. It is speculated that this temporal instability could be a function of the urban nature of the data, possible variations in police-reporting of crash determinants over time, the impact of continuing improvements in vehicle safety features and drivers’ response to them, and/or the effects of macroeconomic instability that was present over the time period considered in this study. Although the source of temporal stability is not clearly known, the general subject of temporal instability warrants substantial attention in future research. The possible presence of temporal instability in injury-severity models can have significant consequences in highway-safety practice where accurate forecasting of the impacts of alternative safety countermeasures is sought.

Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender

This research develops a mixed logit model of driver-injury severity in single-vehicle crashes in California. The research especially considers the heterogeneous effects of age and gender. Older drivers (65+ years old) were found to have a random parameter with about half the population having a higher probability of a fatal injury given a crash than the comparison group of 25–64 year olds with all other factors than age kept constant. The other half of the 65+ population had a lower probability of fatal injury. Heterogeneity was also noted in vehicle age, but related to the gender of the driver, with males linked to, on average, a higher probability of fatal injury in a newer vehicle compared with females, all other factors kept constant. These effects lend support to the use of mixed logit models in injury severity research and show age and gender based population heterogeneity.Several other factors were found to significantly increase the probability of fatal injury for drivers in single-vehicle crashes, most notably: male driver, drunk driving, unsafe speed, older driver (65+) driving an older vehicle, and darkness without streetlights.