Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China

Abstract

The occurrence and severity of truck crashes generally involve complex interactions among factors correlated to driver characteristics, vehicle attributes, roadway geometry and environment conditions. Thus, the elucidation of the significance of these potential contributory factors is critical when developing safety improvement countermeasures. To this end, data from a total of 1175 crashes involving at least one large truck and collected between 2010 and 2015 from two typical freeways in mountainous areas in Jiangxi and Shaanxi (China), were analyzed using a partial proportional odds model to determine the significant risk factors for injury severity of these crashes. Fourteen total explanatory variables, including the age of the driver, seatbelt status, number of vehicle involved, type of transport, freight conditions, brake system status, disregarding speed limit or not, following distance, horizontal roadway alignment, vertical roadway alignment, seasons, day of week, time of crash, and weather were found to significantly affect the severities of the truck crashes. In addition, old drivers, involvement of multiple vehicles, failure to wear seatbelts, overloading, speeding, brake failure and risky following behavior, curve section, seasons (summer, autumn and winter), nighttime period, and adverse weather conditions were also found to significantly increase the likelihood of injury and fatality crashes. Taken together, these findings may serve as a useful guide for developing legislation and technical countermeasures to ensure truck safety on freeways in mountainous regions, particularly in the context of a developing country.

Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach

“…However, the disordered response model fails to reflect the internal order of the accident severity variable, and scholars use the ordered response model instead [ 16 , 17 ]. In addition, the generalized ordered model [ 18 , 19 , 20 ] and random parameter model [ 21 , 22 ] are also introduced. The generalized ordered model is an improvement of the ordered model; it believes that the external variables have different effects on different alternatives, while the random parameter model believes that the parameters in the model are random.…”

Analysis of Factors Contributing to the Injury Severity of Overloaded-Truck-Related Crashes on Mountainous Highways in China

“…On the whole, however, there are few studies that are specific to overloaded trucks, and the existing related works are more focused on the dangers of truck overloading [ 4 , 5 , 8 , 9 , 10 ]. In terms of the injury severity of truck-related crashes, studies oriented to such vehicles are even less common but may include factors such as the size of the load or whether the vehicle is overloaded as an element to examine [ 11 , 12 ]. Unlike previous studies, this paper focuses only on overloaded-truck-related crashes and analyzes the key factors affecting the injury severity of those crashes, based on five aspects: roadway characteristics, environmental characteristics, accident characteristics, vehicle characteristics, and temporal characteristics.…”

Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

“…urban, rural, mountainous freeway) [7,8] or roadway types (e.g. cross or t-intersection, freeways, highways) [9][10][11][12] . A good number of studies have explored and analyzed the spatial clusters of traffic crashes [13][14][15][16] .…”