Exploring Relationships between Months and Different Crash Types on Mountainous Freeways Using a Combined Modeling Approach

Abstract

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.

Predicting the Type of Road Traffic Accident for Test Scenario Generation

“…Furthermore, other research on building classifiers based on police accident data often focuses on predicting or modelling injury-related parameters, for example, the accident severity or the number of injured people [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], or investigating risk factors [30], [31], [32], [33], [34], [35], [36]. Additionally, models regarding crash type, typically describing how road users hit each other, 1 seem to be part of the research focus [37], [38], [39], [40], [41]. In addition, police accident data have been used to predict types of victims involved [42] or to estimate the number of road traffic accidents expected to occur [43].…”