Examining driver injury severity in motor vehicle crashes: A copula-based approach considering temporal heterogeneity in a developing country context.

Using data from a developing country, the current study develops a copula-based joint modeling framework to study crash type and driver injury severity as two dimensions of the severity process. To be specific, a copula-based multinomial logit model (for crash type) and generalized ordered logit model (for driver severity) is estimated in the study. The data for our analysis is drawn from Bangladesh for the years of 2000 to 2015. Given the presence of multiple years of data, we develop a novel spline variable generation approach that facilitates easy testing of variation in parameters across time in crash type and severity components. A comprehensive set of independent variables including driver and vehicle characteristics, roadway attributes, environmental and weather information, and temporal factors are considered for the analysis. The model results identify several important variables (such as driving under the influence of drug and alcohol, speeding, vehicle type, maneuvering, vehicle fitness, location type, road class, road geometry, facility type, surface quality, time of the day, season, and light conditions) affecting crash type and severity while also highlighting the presence of temporal instability for a subset of parameters. The superior model performance was further highlighted by testing its performance using a holdout sample. Further, an elasticity exercise illustrates the influence of the exogenous variables on crash type and injury severity dimensions. The study findings can assist policy makers in adopting appropriate strategies to make roads safer in developing countries.

Identification of factors influencing severity of motorcycle crashes in Dhaka, Bangladesh using binary logistic regression model.

Dhaka, the capital and megacity of the developing country Bangladesh, has experienced a sharp rise in motorcycle users in the last decade, especially after the introduction of ridesharing services. Therefore, the morbidity and mortality rates of motorcycle crash injuries have also increased and become one of the major safety concerns. However, there is scant empirical evidence on motorcycle crash severity in the context of developing countries. Hence, this study was conducted to identify the factors that influenced the severity of motorcycle crashes in Dhaka. A binary logistic regression model was developed using motorcycle crash data of Dhaka over the period of 2006-2015 to identify the contributing factors of motorcycle crash severity. The model output showed that eleven factors significantly increased the probability of fatal motorcycle crashes. These factors were crashes occurring on weekends, during the rainy season, during dawn and night period, at non-intersections, on straight and flat roads, on highways, hit pedestrian type crashes, crashes involving motorcycles with no defect, crashes with heavier vehicles, crashes involving motorcyclists not wearing helmets, and drivers with alcohol suspicion. These findings would help to formulate prevention strategies to reduce the injury severity of motorcycle crashes in the developing countries.