Pedestrians and bicyclists are the most vulnerable road users and suffer the most severe consequences when crashes take place. An extensive literature is available for crash severity in terms of driver safety, but fewer studies have explored non-motorized users' crash severity. Furthermore, most research efforts have examined pedestrian and bicyclist crash severity in urban areas. This study focuses on state roads (mostly outside major urban areas) and aims to identify contributing risk factors of fatal and severe crashes involving pedestrians and bicyclists in state roads. Two ordinal regression models were developed (one for pedestrian and the other for bicyclist crashes) to examine crash severity risk factors. Additional models were developed to investigate road and traffic characteristics that could increase the likelihood of fatal crashes. In the model for pedestrian crash severity risk factors such as age, vehicle type and movement, light conditions, road classification, traffic control device, posted speed limit, location of the pedestrian and wet road surface during clear weather conditions are statistically significant. The bicyclist crash severity model indicates that age, crash location, vehicle movement and alcohol intoxication during dark conditions are statistically significant. In terms of road characteristics and traffic conditions, the models suggested risk factors such as arterials, light conditions, posted speed limit, roadways, and high heavy vehicle volume, increased the odds of a crash being fatal. The results seem to suggest that besides improvements in roadway characteristics, additional countermeasures to reduce crash severity for vulnerable users should include separation of vulnerable users from traffic, educational campaigns, more strict control of alcohol intoxicated drivers, and protection strategies of senior pedestrians.

From the Federal Highway Administration to local departments of transportation, traffic safety is a persistent concern for transportation planners and engineers. Pedestrians are among the most vulnerable road users and require consideration beyond typical analysis of vehicle safety. This study has two objectives: to identify environmental, demographic, and behavioral factors explaining crash severity, and to compare methods for determining the significance of these factors. Binary and ordered logistic regression models were developed and compared to assess factor significance. Environmental and local factors, such as lighting and speed limit, had the strongest correlation with crash severity in all cases. However, inclusion of driver and pedestrian behavior and demographic characteristics improved the fit of the model and, in some cases, predictive ability. The two model types identified the same significant variables in traffic safety, but the magnitudes of the effects differed by model. This finding demonstrates that while the simpler method may yield the same overall results, combining methods can differentiate factors which contribute to the most severe crashes.

This study investigates the fatalities on the National Crash Severity Study (NCSS) of towaway, passenger car accidents. The analysis is in three stages. First, NCSS fatalities are compared to the fatally-injured occupants reported on the Fatal Accident Reporting System (FARS), as a tool for evaluating the representativeness of the NCSS data. Second, estimates of the probability of fatality for NCSS are computed for various conditions, such as the incidence of fire and the sex of the occupant. Third, in cases where two factors are highly correlated, such as is the case for rollover and ejection, modeling techniques are used to help quantify the effects of each variable. The results of this study suggest the following preliminary conclusions: (1) FARS and NCSS have similar distributions of many variables. These include urbanization, size of vehicles, type damage to vehicle, occupant seating location, sex, and restraint use. Differences resulting from the investigative methods and geographical areas of the two studies are identified and assessed. (2) On the NCSS file, many variables are associated with a much higher rate of fatality. These include (a) at the accident level: the number of vehicles involved, urbanization, and the incidence of fire or explosion; (b) at the vehicle level: the change of velocity at impact, the direction of the impacting force, and vehicle damage area; and (c) at the occupant level: seating position, age, sex, ejection, entrapment, and restraint use. (3) Rollover and ejection, which often occur together, are each independently associated with a higher rate of fatality. Of the two factors, ejection appears more related to a higher probability of fatality than does rollover alone. NCSS is the best currently-available source of accident data for analyzing injury-related factors. This report attempts to describe the accidents occurring in the NCSS sampling areas, and suggest ideas for further research.

TRB Special Report 254 - Managing Speed: Review of Current Practices for Setting and Enforcing Speed Limits reviews practices for setting and enforcing speed limits on all types of roads and provides guidance to state and local governments on appropriate methods of setting speed limits and related enforcement strategies. Following an executive summary, the report is presented in six chapters and five appendices.

Identifying factors that affect crash injury severity and understanding how these factors affect injury severity is critical in planning and implementing highway safety improvement programs. Factors which can be categorized in to classes such as driver-related, traffic, environmental and geometric design were considered to develop a statistical model that can be used to predict the effects of these factors on severity of injuries sustained from crashes. Police-reported crash data obtained from the Ohio Department of Public Safety (ODPS) at selected freeway merging and diverging areas in the State of Ohio was used for the development of the model. A generalized ordinal logit model or partial proportional odds model was applied to identify the factors that tend to increase the likelihood of one of five levels of injury severity: No Injuries, Possible/Invisible Injuries, Non-incapacitating Injuries, Incapacitating Injuries, or Fatal Injuries.

Every day thousands of people are killed and injured on our roads. Millions of people each year will spend long weeks in the hospital after severe crashes and many will never be able to live, work or play as they used to do. Current efforts to address road safety are minimal in comparison to this growing human suffering. This report presents a comprehensive overview of what is known about the magnitude, risk factors and impact of road traffic injuries, and about ways to prevent and lessen the impact of road crashes. Over 100 experts, from all continents and different sectors -- including transport, engineering, health, police, education and civil society -- have worked to produce the report. Charts and tables.