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Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. [ABSTRACT FROM AUTHOR]

To investigate pedestrian exposure when only pedestrian-crash data are available, the quasi-induced exposure method is used to identify the factors that contributed to pedestrian-vehicle crashes in Las Vegas from 2004 to 2008. The results show that overall crash severity, light conditions, and weather conditions are potentially risky factors in pedestrian crashes, and the time of day or day of the week are less risky. Multiple-correspondence analysis (MCA) is then used to investigate how pedestrian crash severity is influenced by the interactions among a range of variables. The results indicate that among instances of pedestrian-vehicle crashes cases of property damage only tend to occur during off-peak daytime hours and under dark or dawn light conditions in clear weather. Crashes resulting in injuries usually occur during weekday peak hours in daylight and under rainy conditions. Most fatal crashes occur during off-peak hours, at night on the weekends. [ABSTRACT FROM AUTHOR]

Highlights: [•] Residence characteristics of drivers who caused traffic crashes were investigated. [•] Bayesian Poisson-lognormal model was adopted in this study. [•] Population and commute mode were related with the number of at-fault drivers. [•] Age, commute time, and income were associated with the number of at-fault drivers. [•] Findings from the study can identify residence areas with risky drivers. [Copyright &y& Elsevier]

Minority status and low-income are two important criteria on which environmental-justice areas are being identified. These unique areal characteristics have been of particular interest to traffic safety analysts in investigating pedestrian safety planning factors in deprived areas. In this study, a pedestrian crash model was developed for these areas explicitly accounting for the spatial autocorrelations of different zonal factors commonly used in the traditional transportation planning models. It was found that the Bayesian Poisson-lognormal model with a spatial effect term performed the best compared to the models without accounting for spatial autocorrelations. [ABSTRACT FROM AUTHOR]

Highlights: [•] Traffic congestion causes by traffic accidents contributes to CO2 emissions. [•] More efficient and prompt responses to accidents lead to reduced CO2 emissions. [•] Presentation of case study assessment of CO2 emissions illustrates linkages. [ABSTRACT FROM AUTHOR]

Abstract: Research on weather effects has focused on snow- or rain-related crashes. However, there is a lack of understanding of crashes that occur during fog or smoke (FS). This study presents a comprehensive examination of FS-related crashes using crash data from Florida between 2003 and 2007. A two-stage research strategy was implemented (1) to examine FS-related crash characteristics with respect to temporal distribution, influential factors and crash types and (2) to estimate the effects of various factors on injury severity given that a FS-related crash has occurred. The morning hours from December to February are the prevalent times for FS-related crashes. Compared to crashes under clear-visibility conditions, FS-related crashes tend to result in more severe injuries and involve more vehicles. Head-on and rear-end crashes are the two most common crash types in terms of crash risk and severity. These crashes were more prevalent on high-speed roads, undivided roads, roads with no sidewalks and two-lane rural roads. Moreover, FS-related crashes were more likely to occur at night without street lighting, leading to more severe injuries. [Copyright &y& Elsevier]

Abstract: It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type. [Copyright &y& Elsevier]