Development of Traffic Accident Index Considering Driving Behavior of a Data Based

Abstract

Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

“…Using the ordinal logit model, Bhin and Son [8] analyzed the factors influencing the severity of accidents according to bus type and repetitive accident, and variables such as vehicle-person, speed violation, and signal violation were found to have a significant impact. Lee et al [9] tried to contribute to establishing safer intercity bus policies by developing an accident model for each type of expressway bus accident (driver factor, road, environmental factor, and other factors) through the probability regression model. As a result, in the human factor model, the driver's age and driving experience (more than ten years) were found to be influential factors; significant factors of road and environmental factor models are road conditions (wet and freezing), weather (cloud), nighttime, longitudinal gradients (longer than 5%), and straight road (500 m or more).…”