RESUMEN
INTRODUCTION: Safety of horizontal curves on rural two-lane, two-way undivided roadways is not fully explored. This study investigates factors that impact injury severity of such crashes. METHOD: To achieve the aim of this paper, issues associated with police-reported crash data such as unobserved heterogeneity and temporal stability need to be accounted for. Hence, a mixed logit model was estimated, while heterogeneity in means and variances is investigated by considering four injury severity outcomes for drivers: severe injury, moderate injury, possible injury, and no injury. Crash data for the period between 2011 and 2016 for crashes that occurred in the state of Oregon was analyzed. Temporal stability in factors determining the injury severity was investigated by identifying three time periods through splitting crash data into 2011-2012, 2013-2014, and 2015-2016. RESULTS: Despite some factors affecting injuries in all specified time periods, the values of the marginal effects showed relative differences. The estimation results revealed that some factors increased the risk of being involved in severe injury crashes, including head-on collisions, drunk drivers, failure to negotiate curves, older drivers, and exceeding the speed limits. CONCLUSIONS: The hypothesis that attributes of injury severity are temporally stable is rejected. For example, young drivers (30â¯years old and younger) and middle-aged drivers were found to be temporally instable over time. Practical applications: The findings could help transportation authorities and safety professionals to enhance the safety of horizontal curves through appropriate and effective countermeasures.
Asunto(s)
Accidentes de Tránsito/estadística & datos numéricos , Seguridad/estadística & datos numéricos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Modelos Logísticos , Masculino , Persona de Mediana Edad , Oregon , Factores de Riesgo , Adulto JovenRESUMEN
INTRODUCTION: It is widely agreed that highway work zones pose significant threats to road users because driving conditions in work zones are quite different from the normal ones, particularly when traffic volumes approach a highway capacity. Therefore, work zone safety is a critical aspect for state agencies and traffic engineers. METHOD: In the current study, a total of 10,218 crashes that occurred in highway work zones in the state of Washington for the period between 2007 and 2013 were used. Time of day is disaggregated into four subgroups: (1) Morning from 6:00 to 11:00â¯a.m. (2) Midday from 12:00 to 5:00â¯p.m. (3) Night from 6:00 to 11:00â¯p.m., and (4) Late night from 12:00 to 5:00â¯a.m. Then, four mixed logit models were estimated to account and correct for heterogeneity in the crash data by considering three injury severity levels: severe injury, minor injury, and no injury. RESULTS: The estimation results reveal that most contributing factors are uniquely significant in a specific time of day period, whereas three factors affect injury severity regardless of time of day such as the indicators of not deployed airbag, one passenger vehicle involved in the crash, and rear-end collision. Further, some factors were found to affect injury severity into two or three time periods, such as female drivers that found to decrease the probability of no injury in morning and night time periods, while increasing severe injury outcome in midday time. CONCLUSIONS: The effect of time of day on injury severity of work-zone related crashes should be modeled separately rather than using a holistic model. Practical applications: As a starting point, findings of the current study can be used by transportation officials to reduce fatalities and injuries of work zone crashes by identifying factors that uniquely contribute to each time of day period.
Asunto(s)
Accidentes de Tránsito/estadística & datos numéricos , Conducción de Automóvil/estadística & datos numéricos , Seguridad/estadística & datos numéricos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Modelos Logísticos , Masculino , Persona de Mediana Edad , Probabilidad , Factores de Tiempo , Washingtón , Adulto JovenRESUMEN
In recent years, there has been an increasing interest in understanding the contributory factors to run-off-road (ROR) crashes in the US, especially those where large trucks are involved. Although there have been several efforts to understand large-truck crashes, the relationship between crash factors, crash severity, and ROR crashes is not clearly understood. The intent of this research is to develop statistical models that provide additional insight into the effects that various contributory factors related to the person (driver), vehicle, crash, roadway, and environment have on ROR injury severity. An ordered random parameter probit was estimated to predict the likelihood of three injury severity categories using Oregon crash data: severe, minor, and no injury. The modeling approach accounts for unobserved heterogeneity (i.e., unobserved factors). The results showed that five parameter estimates were found to be random and normally distributed, and varied across ROR crash observations. These were factors related to crashes that occurred between January and April, raised median type, loss of control of a vehicle, the indicator variable of speed not involved, and the indicator variable of two vehicles or more involved in the crashes. In contrast, eight variables were found to be fixed across ROR observations. Looking more closely at the fixed parameter results, large-truck drivers who are not licensed in Oregon have a higher probability of experiencing no injury ROR crash outcomes, and human related factor, fatigue, increases the probability of minor injury. The modeling framework presented in this work offers a flexible methodology to analyze ROR crashes involving large trucks while accounting for unobserved heterogeneity. This information can aid safety planners and the trucking industry in identifying appropriate countermeasures to help mitigate the number and severity of large-truck ROR crashes.