Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

Abstract

Abstract:With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

A Comprehensive Analysis for the Heterogeneous Effects on Driver Injury Severity in Single-Vehicle Passenger Car and SUV Rollover Crashes

“…is the unobserved disturbance term. e traditional ordered logit model arises by assuming the disturbance term to be identically and independently standard logistic distributed [34]. In the framework of traditional ordered logit model, the observed driver injury severity can be mapped from through the threshold , where, is the threshold for splitting the observed injury severity to be estimated and 0 is normalized to 0. e probability of observed injury severity outcomes for driver are described as, where, (⋅) is the cumulative distribution function for the .…”

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

“…Wang et al [22] adopted the classification and regression tree model (CART) to study the association among driving risk, driver/vehicle characteristics, and road environment. Naji et al [23] used the mixed-ordered logit model for evaluating driving risk based on near-crashes. The model was used to examine the contributing factors associated with the driving risk of near-crash events.…”

“…Like [19] and [22], a naturalist study was conducted to collect data; however, self-reported data of historical factors was gathered using a questionnaire. Furthermore, unlike [23,24], hierarchical clustering was used to classify drivers according to their driving risk level. Although the near-crash concept is used as a surrogate measure to evaluate the safety impact of driving behavior in this study, as in [22,24], the parameters used for near-crashes are different, namely braking pressure, time headway, and deceleration.…”

“…To distinguish the driving features of the participants, they are numbered from 1 to 41. Table 1 provides more details about the drivers [23].…”

A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information

“…As a result, the other 80% of crashes are ignored in the TSIP or the identification of hazardous locations, even though the unreported minor crashes could indicate a high risk for injuries or fatal crashes in the 2 of 16 near future [6]. When considering the safety pyramid by Hydén [7], the unreported minor crashes, as well as latent risk factors such as near misses and traffic conflicts, might cause injuries or a major crash [8].…”