Older driver at-fault crashes at unsignalized intersections in Alabama: Injury severity analysis with supporting evidence from a deep learning based approach.

INTRODUCTION: The research described in this paper explored the factors contributing to the injury severity resulting from the male and female older driver (65 years and older) at-fault crashes at unsignalized intersections in Alabama. METHOD: Random parameter logit models of injury severity were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from older driver at-fault crashes. RESULTS: According to these models, some variables were found to be significant only in one model (male or female) but not in the other one. For example, variables such as driver under the influence of alcohol/drugs, horizontal curve, and stop sign were found significant only in the male model. On the other hand, variables such as intersection approaches on tangents with flat grade, and driver older than 75 years were found significant only in the female model. In addition, variables such as making turning maneuver, freeway-ramp junction, high speed approach, and so forth were found significant in both models. Estimation findings showed that two parameters in the male model and another two parameters in the female model could be modeled as random parameters, indicating their varying influences on the injury severity due to unobserved effects. In addition to the random parameter logit approach, a deep learning approach based on Artificial Neural Networks was introduced to predict the outcome of the crashes based on 164 variables that are listed in the crash database. The artificial intelligence (AI)-based method achieved an accuracy of 76% indicating the role of the variables in deciding the final outcome. PRACTICAL APPLICATIONS: Future plans are set to study the use of AI on large sized datasets to achieve a relatively high-performance, and hence to be able to identify which variables contribute the most to the final outcome.

Criteria for Assessing Sustainability of Lignocellulosic Wastes: Applied to the Cellulose Nanofibril Packaging Production in the UK.

Extensive use of petrochemical plastic packaging leads to the greenhouse gas emission and contamination to soil and oceans, posing major threats to the ecosystem. The packaging needs, hence, are shifting to bioplastics with natural degradability. Lignocellulose, the biomass from forest and agriculture, can produce cellulose nanofibrils (CNF), a biodegradable material with acceptable functional properties, that can make packaging among other products. Compared to primary sources, CNF extracted from lignocellulosic wastes reduces the feedstock cost without causing an extension to agriculture and associated emissions. Most of these low value feedstocks go to alternative applications, making their use in CNF packaging competitive. To transfer the waste materials from current practices to the packaging production, it is imperative to assess their sustainability, encompassing environmental and economic impacts along with the feedstock physical and chemical properties. A combined overview of these criteria is absent in the literature. This study consolidates thirteen attributes, delineating sustainability of lignocellulosic wastes for commercial CNF packaging production. These criteria data are gathered for the UK waste streams, and transformed into a quantitative matrix, evaluating the waste feedstock sustainability for CNF packaging production. The presented approach can be adopted to decision scenarios in bioplastics packaging conversion and waste management.

A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama.

The research described in this paper explored the factors contributing to the injury severity resulting from the motorcycle at-fault accidents in rural and urban areas in Alabama. Given the occurrence of a motorcycle at-fault 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 motorcycle at-fault crashes. According to these models, some variables were found to be significant only in one model (rural or urban) but not in the other one. For example, variables such as clear weather, young motorcyclists, and roadway without light were found significant only in the rural model. On the other hand, variables such as older female motorcyclists, horizontal curve and at intersection were found significant only in the urban model. In addition, some variables (such as, motorcyclists under influence of alcohol, non-usage of helmet, high speed roadways, etc.) were found significant in both models. Also, estimation findings showed that two parameters (clear weather and roadway without light) in the rural model and one parameter (on weekend) in the urban model could be modeled as random parameters indicating their varying influences on the injury severity due to unobserved effects. Based on the results obtained, this paper discusses the effects of different variables on injury severities resulting from rural and urban motorcycle at-fault crashes and their possible explanations.

Pedestrian at-fault crashes on rural and urban roadways in Alabama.

The research described in this paper explored the factors contributing to the injury severity resulting from pedestrian at-fault crashes in rural and urban locations in Alabama incorporating the effects of randomness across the observations. Given the occurrence of a crash, random parameter logit models of injury severity (with possible outcomes of major, minor, and possible or no injury) for rural and urban locations were estimated. The estimated models identified statistically significant factors influencing the pedestrian injury severities. The results clearly indicated that there are differences between the influences of a variety of variables on the injury severities resulting from urban versus rural pedestrian at-fault accidents. The results showed that some variables were significant only in one location (urban or rural) but not in the other location. Also, estimation findings showed that several parameters could be modeled as random parameters indicating their varying influences on the injury severity. Based on the results obtained, this paper discusses the effects of different variables on pedestrian injury severities and their possible explanations. From planning and policy perspective, the results of this study justify the need for location specific pedestrian safety research and location specific carefully tailored pedestrian safety campaigns.

Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama.

The research described in this paper analyzed injury severities at a disaggregate level for single-vehicle (SV) and multi-vehicle (MV) large truck at-fault accidents for rural and urban locations in Alabama. Given the occurrence of a crash, four separate random parameter logit models of injury severity (with possible outcomes of major, minor, and possible or no injury) were estimated. The models identified different sets of factors that can lead to effective policy decisions aimed at reducing large truck-at-fault accidents for respective locations. The results of the study clearly indicated that there are differences between the influences of a variety of variables on the injury severities resulting from urban vs. rural SV and MV large truck at-fault accidents. The results showed that some variables were significant only in one type of accident model (SV or MV) but not in the other accident model. Again, some variables were found to be significant in one location (rural or urban) but not in other locations. The study also identified important factors that significantly impact the injury severity resulting from SV and MV large truck at-fault accidents in urban and rural locations based on the estimated values of average direct pseudo-elasticity. A careful study of the results of this study will help policy makers and transportation agencies identify location specific recommendations to increase safety awareness related to large truck involved accidents and to improve overall highway safety.

Driver aging and its effect on male and female single-vehicle accident injuries: some additional evidence.

INTRODUCTION: This study explores the differences in injury severity between male and female drivers, and across the different age groups, in single-vehicle accidents involving passenger cars. METHOD: Given the occurrence of an accident, separate male and female multinomial logit models of injury severity (with possible outcomes of no injury, injury, and fatality) were estimated for young (ages 16 to 24), middle-aged (ages 25 to 64), and older (ages 65 and older) drivers. RESULTS: The estimation results show statistically significant differences in the factors that determine injury-severity levels between male and female drivers and among the different driver age groups. CONCLUSIONS: We discuss a number of plausible explanations for the observed age/gender differences and provide suggestions for future work on the subject. IMPACT ON INDUSTRY: A better understanding of age and gender differences can lead to improvements in vehicle and highway design to minimize driver injury severity. This paper provides some new evidence to help unravel this complex problem.