Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

Abstract

Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

“…However, few efforts were made to identify the influence of collision order, and the differences in injury severity between P2T and T2P rear-end crashes were largely overlooked. In light of the above discussion, this study assumes that there are unique contributing factors between truck as a leading vehicle and passenger car as a leading vehicle in rear-end crashes, as the similar results illustrated in other comparison studies [10,11,26,27]. Specifically, the objective of this study is twofold: (i) to investigate the differences of effects of factors that contribute to injury severity in truck-involved rear-end collisions, and (ii) to compare the model performance between combined dataset (model truck-involved rear-end crashes as a whole) and separate dataset (model P2T crashes and T2P crashes separately).…”

A Random Parameter Logit Model of Immediate Red-Light Running Behavior of Pedestrians and Cyclists at Major-Major Intersections

“…In the light of these two defects discussed above, there are some researchers who have improved their studies by using the approach of RPLM when investigating the effect of the factors on dependent variables in other traffic research areas. Anastaspopoulos et al [18] investigated the effect of the related factors on the delay of highway project using the Journal of Advanced Transportation 3 [17,[20][21][22][23][24]. However, few researches apply RPLM to pedestrians' and (electric) bicyclists' IRLR behaviours at intersections.…”

“…where ( = 1) is the probability of running the red light immediately of observation n. f ( | ) is the probability density function of , which is usually specified to be continuously distributed in most applications, such as normal, lognormal, triangular, and uniform function (for more details see Hensher et al [38]). In the present research, random parameters are assumed to be normally distributed, which is a widely used assumption in previous studies [22,24]. is the vector of parameters of the density function (such as mean and variance).…”

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

“…e results showed that the number of lanes and average speed at intersections would have a significant impact on the crash probability [9]. ere are also some scholars who have studied the prediction methods based on dividing crashes into single-and multivehicle crashes [10], but there are few studies conducted on the collision type (rear-end collision, side-impact collision, etc. ), particularly in China.…”