A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model

Korea Research Institute for Human Settlements, National Infrastructure & GIS Research Division, 224 Simin-Ro, Dongan-gu, Anyang-si, Gyeonggi-do 431-712, Republic of Korea.
Accident; analysis and prevention (Impact Factor: 1.65). 11/2010; 42(6):1751-8. DOI: 10.1016/j.aap.2010.04.016
Source: PubMed

ABSTRACT Pedestrian-injury severity has been traditionally modeled with approaches that have assumed that the effect of each variable is fixed across injury observations. This assumption ignores possible unobserved heterogeneity which is likely to be particularly important in pedestrian injuries because unobserved physical health, strength, and behavior may significantly affect the pedestrians' ability to absorb collision forces. To address such unobserved heterogeneity, this research applies a mixed logit model to analyze pedestrian-injury severity in pedestrian-vehicle crashes. Using police-reported collision data from 1997 through 2000 from North Carolina, several factors were found to more than double the average probability of fatal injury for pedestrians in motor-vehicle crashes including: darkness without streetlights (400% increase in fatality probability), vehicle is a truck (370% increase), freeway (330% increase), speeding involved (360% increase), and collisions involving a motorist who had been drinking (250% increase). It was also found that the effect of pedestrian age was normally distributed across observations, and that as pedestrians became older the probability of fatal injury increased substantially. Heterogeneity in the mean of the random parameters for the freeway and pedestrian-solely-at-fault collision indicators was related to pedestrian gender, and heterogeneity in the mean of the random parameters for the traffic-sign and motorist-back-up indicators was related to pedestrian age.

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    • "Risk factors for death/severe injury in pedestrian–motor vehicle accidents The factors that affect death/severe injury in pedestrian accidents include accident liability, accident types, pedestrians' characteristics and behavior, drivers' behavior, vehicle types/ conditions and the environment (Kim et al., 2008a,b; Damsere- Derry et al., 2003). Age and physical conditions are identified as the main risk factors (Sklar et al., 1989; Vestrup and Reid, 1989; Zegeer et al., 1996; Fontaine and Gourlet, 1997; Harruff et al., 1998; Al- Ghamdi, 2002; Retting, 2003; Chong et al., 2010; Kim et al., 2010). Speeding and drunk driving also increased the probability of severe injury to pedestrians (Miles-Doan, 1996; Jensen, 1999; Öström and Eriksson, 2001; Kim et al., 2008a,b; Damsere-Derry et al., 2003). "
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    ABSTRACT: The number of pedestrian–motor vehicle accidents and pedestrian deaths in China surged in recent years. However, a large scale empirical research on pedestrian traffic crashes in China is lacking. In this study, we identify significant risk factors associated with fault and severity in pedestrian–motor vehicle accidents. Risk factors in several different dimensions, including pedestrian, driver, vehicle, road and environmental factors, are considered. We analyze 6967 pedestrian traffic accident reports for the period 2006–2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report. Pedestrian traffic crashes have a unique inevitability and particular high risk, due to pedestrians’ fragility, slow movement and lack of lighting equipment. The empirical analysis of the present study has the following policy implications. First, traffic crashes in which pedestrians are at fault are more likely to cause serious injuries or death, suggesting that relevant agencies should pay attention to measures that prevent pedestrians from violating traffic rules. Second, both the attention to elderly pedestrians, male and experienced drivers, the penalty to drunk driving, speeding, driving without a driver's license and other violation behaviors should be strengthened. Third, vehicle safety inspections and safety training sessions for truck drivers should be reinforced. Fourth, improving the road conditions and road lighting at night are important measures in reducing the probability of accident casualties. Fifth, specific road safety campaigns in rural areas, and education programs especially for young children and teens should be developed and promoted. Moreover, we reveal a country-specific factor, hukou, which has significant effect on the severity in pedestrian accidents due to the discrepancy in the level of social insurance/security, suggesting that equal social security level among urban and rural people should be set up. In addition, establishing a comprehensive liability distribution system for non-urban areas and roadways will be conducive to both pedestrians’ and drivers’ voluntary compliance with traffic rules.
    Accident Analysis & Prevention 09/2014; 73:141–150. DOI:10.1016/j.aap.2014.08.018 · 1.87 Impact Factor
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    • "In this study, mixed logit models are developed to identify significant casual factors and quantify their impacts on driver injury severities in SV and MV crashes on rural two-lane highways. Following the previous work of Kim et al. (2010), Milton et al. (2008), Pai and Saleh (2008) and Washington et al. (2003), mixed logit models are developed as follows. "
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    ABSTRACT: Crashes occurring on rural two-lane highways are more likely to result in severe driver incapacitating injuries and fatalities. In this study, mixed logit models are developed to analyze driver injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes on rural two-lane highways in New Mexico from 2010 to 2011. A series of significant contributing factors in terms of driver behavior, weather conditions, environmental characteristics, roadway geometric features and traffic compositions, are identified and their impacts on injury severities are quantified for these two types of crashes, respectively. Elasticity analyses and transferability tests were conducted to better understand the models' specification and generality. The research findings indicate that there are significant differences in causal attributes determining driver injury severities between SV and MV crashes. For example, more severe driver injuries and fatalities can be observed in MV crashes when motorcycles or trucks are involved. Dark lighting conditions and dusty weather conditions are found to significantly increase MV crash injury severities. However, SV crashes demonstrate different characteristics influencing driver injury severities. For example, the probability of having severe injury outcomes is higher when vans are identified in SV crashes. Drivers' overtaking actions will significantly increase SV crash injury severities. Although some common attributes, such as alcohol impaired driving, are significant in both SV and MV crash severity models, their effects on different injury outcomes vary substantially. This study provides a better understanding of similarities and differences in significant contributing factors and their impacts on driver injury severities between SV and MV crashes on rural two-lane highways. It is also helpful to develop cost-effective solutions or appropriate injury prevention strategies for rural SV and MV crashes.
    Accident Analysis & Prevention 07/2014; 72C:105-115. DOI:10.1016/j.aap.2014.06.014 · 1.87 Impact Factor
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    • "Mixed logit models, which can address these limitations and consider the random effects of variables, have recently been adopted in the studies on accident injury (e.g. Milton et al., 2008; Kim et al., 2010; Malyshkina and Mannering, 2010; Moore et al., 2010). For example , Moore et al. (2010) applied mixed logit model to compare the statistical difference of bicyclist injury severity from motor vehicle crashes at intersection and non-intersection locations. "
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    ABSTRACT: In adverse driving conditions, such as inclement weather and/or complex terrain, trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the difference in driver-injury severity between SV and MV accidents by using mixed logit models. Injury severity from SV and MV accidents involving trucks on rural highways is modeled separately and their respective critical risk factors such as driver, vehicle, temporal, roadway, environmental and accident characteristics are evaluated. It is found that there exists substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies can be made. Estimation findings indicate that the snow road surface and light traffic indicators will be better modeled as random parameters in SV and MV models respectively. As a result, the complex interactions of various variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the injury severity of truck drivers from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future.
    Accident; analysis and prevention 09/2011; 43(5):1677-88. DOI:10.1016/j.aap.2011.03.026 · 1.65 Impact Factor
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