Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Development of safety performance functions for SafetyAnalyst applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

An examination of psychological meaningfulness, safety, and availability as the underlying mechanisms linking job features and personal characteristics to work engagement

The present study tested a nomological net of work engagement that was derived from its extant research. Two of the main work engagement models that have been presented and empirically tested in the literature, the JD-R model and Kahn's model, were integrated to test the effects that job features and personal characteristics can have on work engagement through the psychological conditions of meaningfulness, safety, and availability. In this study, safety refers to psychological perceptions of safety and not workplace safety behaviors. The job features that were tested in this model included person-job fit, autonomy, co-worker relations, supervisor support, procedural justice, and interactional justice, while the personal characteristics consisted of self-consciousness, self-efficacy, extraversion, and neuroticism. Thirty-four hypotheses and a conceptual model were tested in order to establish the viability of this nomological net of work engagement in which it was expected that meaningfulness would mediate the relationships between job features and work engagement, safety would mediate the relationships that job features and personal characteristics have with work engagement, and availability (physical, emotional, and cognitive resources) would mediate the relationships that personal characteristics have with work engagement. Furthermore, analyses were run in order to determine the factor structure of work engagement, assess whether or not it exhibits differential validity from organizational commitment and job satisfaction, and confirm that it is positively related to the outcome variable of organizational citizenship behavior (OCB). The final sample consisted of 500 workers from an online labor market who responded to a questionnaire composed of measures of all constructs included in this study. Findings show that work engagement is best represented as a three-factor construct, composed of vigor, dedication and absorption. Furthermore, support was found for the distinction of work engagement from the related constructs of organizational commitment and job satisfaction. With regard to the proposed model, meaningfulness proved to be the strongest predictor of work engagement. Results show that it partially mediates the relationships that all job features have with work engagement. Safety proved to be a partial mediator of the relationships that autonomy, co-worker relations, supervisor support, procedural justice, interactional justice, and self-efficacy have with work engagement, and fully mediate the relationship between neuroticism and work engagement. Findings also show that availability partially mediates the positive relationships that extraversion and self-efficacy have with work engagement, and fully mediates the negative relationship that neuroticism has with work engagement. Finally, a positive relationship was found between work engagement and OCB. Research and organizational implications are discussed.

An Examination of Psychological Meaningfulness, Safety, and Availability as the Underlying Mechanisms linking Job Features and Personal Characteristics to Work Engagement

The present study tested a nomological net of work engagement that was derived from its extant research. Two of the main work engagement models that have been presented and empirically tested in the literature, the JD-R model and Kahn’s model, were integrated to test the effects that job features and personal characteristics can have on work engagement through the psychological conditions of meaningfulness, safety, and availability. In this study, safety refers to psychological perceptions of safety and not workplace safety behaviors. The job features that were tested in this model included person-job fit, autonomy, co-worker relations, supervisor support, procedural justice, and interactional justice, while the personal characteristics consisted of self-consciousness, self-efficacy, extraversion, and neuroticism. Thirty-four hypotheses and a conceptual model were tested in order to establish the viability of this nomological net of work engagement in which it was expected that meaningfulness would mediate the relationships between job features and work engagement, safety would mediate the relationships that job features and personal characteristics have with work engagement, and availability (physical, emotional, and cognitive resources) would mediate the relationships that personal characteristics have with work engagement. Furthermore, analyses were run in order to determine the factor structure of work engagement, assess whether or not it exhibits differential validity from organizational commitment and job satisfaction, and confirm that it is positively related to the outcome variable of organizational citizenship behavior (OCB). The final sample consisted of 500 workers from an online labor market who responded to a questionnaire composed of measures of all constructs included in this study. Findings show that work engagement is best represented as a three-factor construct, composed of vigor, dedication and absorption. Furthermore, support was found for the distinction of work engagement from the related constructs of organizational commitment and job satisfaction. With regard to the proposed model, meaningfulness proved to be the strongest predictor of work engagement. Results show that it partially mediates the relationships that all job features have with work engagement. Safety proved to be a partial mediator of the relationships that autonomy, co-worker relations, supervisor support, procedural justice, interactional justice, and self-efficacy have with work engagement, and fully mediate the relationship between neuroticism and work engagement. Findings also show that availability partially mediates the positive relationships that extraversion and self-efficacy have with work engagement, and fully mediates the negative relationship that neuroticism has with work engagement. Finally, a positive relationship was found between work engagement and OCB. Research and organizational implications are discussed.

Safety habits promoted by Hispanic parents for school-aged and adolescent children

Accidents are the leading cause of death among children. Compliance with safety measures have been shown to reduce morbidity and mortality in children. However, Hispanic parents and children have not been adequately studied in the literature in regard to trauma and safety habits utilized. In this exploratory-descriptive study, a convenience sample of 48 Hispanic parents were questioned about their child's activities. The purpose was to determine what safety equipment was used during these activities and how often they were used. Responses to an English/Spanish questionnaire were summarized by frequencies and percentages. Findings suggested that these Hispanic children were at risk for injury due to a low percentage of children wearing the necessary protective equipment. Therefore, education must stress consistent use of safety equipment if injuries are to be prevented. ^

Achieving zero accidents—A strategic framework for continuous safety improvement in the construction industry

In the U.S., construction accidents remain a significant economic and social problem. Despite recent improvement, the Construction industry, generally, has lagged behind other industries in implementing safety as a total management process for achieving zero accidents and developing a high-performance safety culture. One aspect of this total approach to safety that has frustrated the construction industry the most has been “measurement”, which involves identifying and quantifying the factors that critically influence safe work behaviors. The basic problem attributed is the difficulty in assessing what to measure and how to measure it—particularly the intangible aspects of safety. Without measurement, the notion of continuous improvement is hard to follow. This research was undertaken to develop a strategic framework for the measurement and continuous improvement of total safety in order to achieve and sustain the goal of zero accidents, while improving the quality, productivity and the competitiveness of the construction industry as it moves forward. The research based itself on an integral model of total safety that allowed decomposition of safety into interior and exterior characteristics using a multiattribute analysis technique. Statistical relationships between total safety dimensions and safety performance (measured by safe work behavior) were revealed through a series of latent variables (factors) that describe the total safety environment of a construction organization. A structural equation model (SEM) was estimated for the latent variables to quantify relationships among them and between these total safety determinants and safety performance of a construction organization. The developed SEM constituted a strategic framework for identifying, measuring, and continuously improving safety as a total concern for achieving and sustaining the goal of zero accidents.

Research-to-Practice (R2P) Tools for Improving Safety in Nighttime Highway Construction Work Zones

The safety of workers in nighttime roadway work zones has become a major concern for state transportation agencies due to the increase in the number of work zone fatalities. During the last decade, several studies have focused on the improvement of safety in nighttime roadway work zones; but the element that is still missing is a set of tools for translating the research results into practice. This paper discusses: 1) the importance of translating the research results related to the safety of workers and safety planning of nighttime work zones into practice, and 2) examples of tools that can be used for translating the results of such studies into practice. A tool that can propose safety recommendations in nighttime work zones and a web-based safety training tool for workers are presented in this paper. The tools were created as a component of a five-year research study on the assessment of the safety of nighttime roadway construction. The objectives of both tools are explained as well as their functionalities (i.e., what the tools can do for the users); their components (e.g., knowledge base, database, and interfaces); and their structures (i.e., how the components of the tools are organized to meet the objectives). Evaluations by the proposed users of each tool are also presented.

Personnel selection, safety performance, and job performance: Are safe workers better workers?

The present dissertation consists of two studies that combine personnel selection, safety performance, and job performance literatures to answer an important question: are safe workers better workers? Study 1 tested a predictive model of safety performance to examine personality characteristics (conscientiousness and agreeableness), and two novel behavioral constructs (safety orientation and safety judgment) as predictors of safety performance in a sample of forklift loaders/operators (N = 307). Analyses centered on investigating safety orientation as a proximal predictor and determinant of safety performance. Study 2 replicated Study 1 and explored the relationship between safety performance and job performance by testing an integrative model in a sample of machine operators and construction crewmembers (N = 323). Both Study 1 and Study 2 found conscientiousness, agreeableness, and safety orientation to be good predictors of safety performance. While both personality and safety orientation were positively related to safety performance, safety orientation proved to be a more proximal determinant of safety performance. Across studies, results surrounding safety judgment as a predictor of safety performance were inconclusive, suggesting possible issues with measurement of the construct. Study 2 found a strong relationship between safety performance and job performance. In addition, safety performance served as a mediator between predictors (conscientiousness, agreeableness and safety orientation) and job performance. Together these findings suggest that safe workers are indeed better workers, challenging previous viewpoints to the contrary. Further, results implicate the viability of personnel selection as means of promoting safety in organizations.^

[Poster] Friends of the Island

This poster presentation will exhibit the ongoing design work within two small islands adjacent to Diner Key Marina gifted by the City of Miami to the non-profit organization, Shake-A-Leg. This organization works to blur the lines between access, recreation, and skill, redefining what is possible. Abled and disabled children, adolescents, and young adults go to Shake-A-Leg to participate in a myriad of activities such as sailing, kayaking, swimming, painting, and socializing in these islands regardless of their inadequate walkways and installations. The users are people from different social statuses, physical and mental abilities. This poster will exhibit the design proposal to enhance the islands’ infrastructure to provide seamless access to all its visitors through the lush habitat in these islands. The proposed program challenges the disabilities of the person and helps them to mediate and transform them into new possibilities and abilities. The program proposes ten components which include a nature trail, restrooms, dinning center, art studio, and observation deck. The design of each space draws from the sailing boat to create solutions were the user will explore, participate and enjoy. The location encourages a feeling of challenge but at the same time it is a place for relief and freedom, allowing them to discover the diversity through experimentation and interaction with the environment. This poster presents design principles that value equal access and experience, celebrating the differences among users and the environment, blurring the lines between nature and access.