Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents

Chang, Li-Yen; Mannering, Fred

doi:10.1016/s0001-4575(99)00014-7

Cited by 264 publications

(129 citation statements)

References 34 publications

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“…Since the main purpose of this paper is accident prediction, the specification of the mixed logit model used seems appropriate. consistent with the previous study by Chang and Mannering (1999) who found that interstate highways are more likely to result in property damage only accidents instead of possible injury or injury/fatal accidents. Raining weather has been found to decrease the probability of serious injury and fatal accidents and increase the probability of slight injury accidents, which may be due to lower driving speed in rainy weather.…”

Section: Accident Severity Analysissupporting

confidence: 92%

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Wang

Quddus

Ison

2011

Accident Analysis & Prevention

119

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Citation: WANG, C., QUDDUS, M.A. and ISON, S.G., Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model. Accident Analysis and Prevention, 43(6), pp.1979-1990 Metadata AbstractAccident prediction models (APMs) have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poissonlognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the twostage model has also been compared with other ranking methods, such as the naïve ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.

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Section: Accident Severity Analysissupporting

confidence: 92%

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Wang

Quddus

Ison

2011

Accident Analysis & Prevention

119

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“…First, except for the studies by Chang and Mannering (1999) and Ulfarsson and Mannering (2004), none of the earlier studies has comprehensively considered all the five categories of variables. Second, the two most prevalent structures used to examine injury severity are logistic regression models and orderedresponse models.…”

Section: Seat Belt Use Not Considered Endogenous To the Modeling Frammentioning

confidence: 99%

“…Traffic crashes result in several fatalities everyday on U.S. roadways, and those who manage to survive crashes are faced with such potential consequences as mental trauma, pain, expensive medical costs, and increased insurance premiums (Cohen and Einav, 2003;Chang and Mannering, 1999). The society as a whole is also at a loss, both economically and emotionally, because of these incidents.…”

Section: Introductionmentioning

confidence: 99%

A joint econometric analysis of seat belt use and crash-related injury severity

Eluru

Bhat

2007

Accident Analysis & Prevention

190

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This paper formulates a comprehensive econometric structure that recognizes two important issues in crash-related injury severity analysis. First, the impact of a factor on injury severity may be moderated by various observed and unobserved variables specific to an individual or to a crash. Second, seat belt use is likely to be endogenous to injury severity. That is, it is possible that intrinsically unsafe drivers do not wear seat belts and are the ones likely to be involved in high injury severity crashes because of their unsafe driving habits.The preceding issues are considered in the current research effort through the development of a comprehensive model of seat belt use and injury severity that takes the form of a joint correlated random-coefficients binary-ordered response system. To our knowledge, this is the first instance of such a model formulation and application not only in the safety analysis literature, but in the econometrics literature in general. The empirical analysis is based on the 2003 General Estimates System (GES) data base. Several types of variables are considered to explain seat belt use and injury severity levels, including driver characteristics, vehicle characteristics, roadway design attributes, environmental factors, and crash characteristics. The results, in addition to confirming the effects of various explanatory variables, also highlight the importance of (a) considering the moderating effects of unobserved individual/crash-related factors on the determinants of injury severity and (b) seat belt use endogeneity. From a policy standpoint, the results suggest that seat belt non-users, when apprehended in the act, should perhaps be subjected to both a fine (to increase the chances that they wear seat belts) as well as mandatory enrollment in a defensive driving course (to attempt to change their aggressive driving behaviors).

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“…Abdelwahab and Abdel-Aty (2001) look at determinants of driver injury severity in traffic accidents at signalized intersections in an area of Central Florida. Some focus solely on determinants of truck-involved crash rates (Golob and Regan, 2004;Charbotel et al, 2003;Chang and Mannering, 1999). Studies of the role of weather and lighting conditions include Edwards (1998) and Golob and Recker (2003).…”

Section: Micro-level Studies Of the Causes Of Accidents Or Fatalitiesmentioning

confidence: 99%

Traffic Fatalities and Economic Growth

Kopits¹,

Cropper²

2003

Policy Research Working Papers

214

218

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This dissertation adds to the current understanding of the socioeconomic determinants of traffic fatality rates by examining the variation in road deaths across countries and over time. Chapter two investigates the relationship between road safety and economic development within an Environmental Kuznets Curve framework. Reduced-form models of fatality risk as a function of per capita income are estimated using panel data for over 80 countries. The results confirm an inverted U-shaped relationship with income, with traffic fatality risk (fatalities per population) beginning to decline at incomes similar to those found for several environmental externalities. The turning point is driven by the rate of decline in fatalities per vehicle as income rises, since motorization rates (vehicles per population), while increasing with income at a decreasing rate, never decline with economic growth. This suggests that road safety improvements accompanying income growth depend on policies that reduce fatalities per vehicle rather than on reducing motorization.Projections suggest that the global road death toll will grow by 66% between 2000 and 2020 if historic trends continue. This number, however, reflects divergent rates of change in different parts of the world.Chapter three focuses on factors underlying the decline in fatalities per distance traveled, i.e., the downward sloping part of the road safety Kuznets curve.Formal models of traffic fatalities are developed for vehicle occupants and pedestrians. Reduced-form approximations to these models are estimated using panel data for over 30 high-income countries over 1964-2002. Estimates suggest that demographic changes and road building contributed to declines in both vehicle occupant and non-occupant fatality rates. In addition, increases in the size of the vehicle fleet, as predicted by theory, have increased occupant deaths while decreasing pedestrian deaths per distance driven.Improvements in medical care also played a role in reducing both fatality rates. The results do not offer evidence of a significant increase in demand for risky driving in response to newer, safer vehicles or seatbelt usage. The extent to which alcohol and increases in the elderly population have detrimental effects on fatality rates is found to be twice as large for pedestrians as for vehicle occupants.

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Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents

Cited by 264 publications

References 34 publications

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model

A joint econometric analysis of seat belt use and crash-related injury severity

Traffic Fatalities and Economic Growth

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