Investigating the severity of expressway crash based on the random parameter logit model accounting for unobserved heterogeneity

Abstract

The present study utilized a random parameter logit (RPL) model to explore the nonlinear relationship between explanatory variables and the likelihood of expressway crash severity. The potential unobserved heterogeneity of data brought by China’s road traffic characteristics was fully considered. A total of 1154 crashes happened on Hang-Jin-Qu Expressway from 2013 to 2018 were analyzed. In addition to the conventional impact factors considered in the past, variables related to road geometry were also introduced, which contributed to expressway accidents significantly. The overall stability of the model estimation was examined by likelihood ratio test. Then, the average elastic coefficient of the significant factors at each severity level was also calculated. Several factors that significantly increase the fatal crash probability were highlighted: rainy/snowy/cloudy weather condition, low visibility (100– m), night without light, wet-skid road surface, being female, aged 41+ years, collision with a rigid barrier and some other obstacles, radius and length of horizontal curve, and longitudinal gradient. The parameters of four factors were random and obeyed normal distribution: night without light, being female, driving experience with 10 + years and with large vehicle responsible. These findings provide insights for better understanding of expressway crash severity. Some countermeasures were proposed about driver education, traffic law enforcement, vehicle and road design, environmental improvement, and so on.

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

“…There are many models used to search and identify the element of risk related to the casualties of road traffic accidents, including regression (the ordered probit model, the mixed logit model, etc.) [9][10][11][12][13][14][15][16][17], hierarchical linear modeling [18], quantitative risk assessment [19], latent class clustering analysis [20], partial least squares path model [21], random parameter logit model [22], etc. However, most statistical models have assumed the basic relationship between independent variables and dependent variables [22,23], while machine learning does not require any assumptions about the basic relationship between variables [24].…”

“…[9][10][11][12][13][14][15][16][17], hierarchical linear modeling [18], quantitative risk assessment [19], latent class clustering analysis [20], partial least squares path model [21], random parameter logit model [22], etc. However, most statistical models have assumed the basic relationship between independent variables and dependent variables [22,23], while machine learning does not require any assumptions about the basic relationship between variables [24]. Some researchers have explored salient features that may affect the severity of automobile accidents [25,26] and have applied machine learning to the prediction and control of intelligent transportation systems (ITS) in previous research.…”

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

“…The literature on expressway collisions has shown that road user, collision, traffic flow, road, and environmental characteristics are the variables that are closely related to the incidence and severity of expressway collisions. In terms of road user characteristics, gender, age, and the driver’s experience were found to be variables that were strongly associated with the severity of injuries in expressway collisions [ 6 , 7 ]. Collision characteristics including the crash’s location, time, and season have been confirmed to have a significant correlation with the severity of injuries on expressways [ 8 ].…”

“…In addition, the incidence rate and severity of expressway traffic collisions are also significantly different in different road environments [ 7 ]. Qu et al [ 12 ] explored the risk impact of ramps on various types of locations across distinct traffic lanes (shoulder lane, middle lane, and median lane) and found that median lanes and sections after off-ramps have relatively lower risks compared to other lanes and sections.…”

Accident Severity Analysis On the North-South Expressway Using Binomial Logistic Regression

“…Expressway was chosen for this study because severe accidents mostly occurred on expressways, and the posted speed on expressways is higher than on federal and state roads. Expressway has lower accident rate compared to federal and state roads, yet it is frequently ranked first among all road types in terms of deaths [1]. According to Darma [2], the rate of deaths per kilometer on expressways in Malaysia is 0.404, which is the highest compared to federal and state roads.…”