The research described in this paper explored the factors contributing to injury severity resulting from rollover accidents of light trucks—SUVs and pickups in particular—in Alabama. Given the occurrence of a rollover crash, random parameter logit models of injury severity (with possible outcomes of fatal, major, minor, and possible or no injury) were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from SUV and pickup rollover crashes. According to these models, some variables were significant in one model (SUV or pickup) but not the other. For example, variables such as roadway downgrade, female drivers, and daylight were significant only in the SUV model. Variables such as driver fatigue, driver or occupant wearing a seat belt, and freeway were significant only in the pickup model. In addition, some variables (such as not wearing a seat belt, two-lane roadway, horizontal curve, and old driver) were significant in both models. Estimation findings showed that two parameters (horizontal curve and at intersections) in the SUV model and two parameters (horizontal curve and dry roadway surface) in the pickup model could be modeled as random parameters, indicating their varying influence on the injury severity related to unobserved effects. The results obtained are used in this paper for a discussion of the effects of variables on pedestrian and bicyclist injury severities and their possible explanations.
Rural four-lane roadways provide important transportation accessibility and mobility to populations in rural areas. It is a challenge for practitioners to determine cross-section types when both benefits and costs need to be considered. Crash Modification Factors (CMFs) are developed to evaluate the safety effectiveness of alternative designs. However, safety effectiveness could vary significantly across contexts. Thus, this study aims to estimate CMFs for alternative cross sections of rural four-lane roadways under different contexts characterized by traffic volume, truck percentage, and access point density. Using Georgia state-wide crash data, this study developed Safety Performance Functions (SPFs) to predict crash frequencies for different contexts. Considering linearity and independence assumptions of traditional negative binomial SPFs, this study adopts Bayesian generalized negative binomial modeling approaches to relax those assumptions and only follows the Bayes rule to form SPFs for CMF estimation. This study focuses on four typical cross-sections including: (1) non-traversable medians; (2) two-way-left-turn lanes; (3) 4-ft flush medians; and (4) undivided roadways with double-yellow lines (the base cross-section design). The results show that CMFs vary significantly across different contexts. Compared with the base cross-section design, safety benefits of the other three designs can be either positive or negative under different traffic or road conditions. For example, 4-ft flush medians are found to have positive safety benefits (CMF < 1) under lower average daily traffic volumes (e.g., ≤ 6,000) and negative benefits (CMF > 1) under greater average daily traffic volumes (e.g., ≥ 15,000). The findings suggest that, to enhance roadway safety, practitioners should vary cross-section designs for different rural contexts.
State departments of transportation across the nation fund selective enforcement campaigns aimed at intensifying law enforcement at certain locations to improve traffic safety. At the same time, many states passively collect large data sets, such as officer GPS location tracks. To evaluate the effectiveness of selective enforcement, an approach was developed to employ structured query language (SQL) and geographic information system (GIS) technology to mine and integrate police patrol patterns, citations issued, crash occurrences, and selective enforcement periods. This information was analyzed in a relational database within a spatial and temporal analysis framework. The intent was to use solely GIS technology; however, the size of the data sets was prohibitive, and SQL was used as a big data analytic. The SQL techniques successfully turned more than 37 million points of GPS data into 1.3 million points of selective enforcement location information, enabled the geolocation of 72.6% of electronic citations, and identified 21 selective enforcement areas across the state of Alabama. With big data analytics, GIS technology was reestablished as useful for the evaluation of changes in crash and citation frequencies before and during selective enforcement. Paired difference t-tests confirmed the decrease of crash frequency with 85% confidence at urban and rural locations. The analysis of the number of citations at the locations confirmed that citations increased during selective enforcement by an average of 254%. The developed methodology is a successful approach using large data sets for an unintended purpose to make valuable engineering conclusions and data-driven discoveries.
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