Work Zone Safety Analysis, Investigating Benefits from Accelerated Bridge Construction (ABC) on Roadway Safety

The attributes of work zones have significant impacts on the risk of crash occurrence. Therefore, identifying the factors associated with crash severity and frequency in work zone locations is of important value to roadway safety. In addition, the significant loss of workers’ lives and injuries resulting from work zone crashes indicates the emergent need for a comprehensive and in-depth investigation of work zone crash mechanisms. The cost of work zone crashes is another issue that should be taken into account as work zone crashes impose millions of dollars on society each year. Applying innovative construction methods like Accelerated Bridge Construction (ABC) dramatically decreases on-site construction duration and thus improves roadway safety. This safe and cost-effective procedure for building new bridges or replacing/rehabilitating existing bridges in just a few weeks instead of months or years may prevent crashes and avoid injuries as a result of work zone presence. The application of machine learning techniques in traffic safety studies has seen explosive growth in recent years. Compared to statistical methods, MLs are more accurate prediction models due to their ability to deal with more complex functions. To this end, this study focuses on three major areas: crash severity at construction work zones with worker presence, crash frequency at bridge locations, and assessment of the associated costs to calculate the contribution of safety to the benefit-cost ratio of ABC as compared to conventional methods. Some key findings of this study can be highlighted as in-depth investigation of contributing factors in conjunction with the results from statistical and machine learning models, which can provide a more comprehensive interpretation of crash severity/frequency outcomes. The demonstration of work zone crashes needs to be modeled separately by time of day for severity analysis with a high level of confidence. Investigation of the contributing factors revealed the nonlinear relationship between crash severity/frequency and contributing factors. Finally, the results showed that the safety benefits from a case study in Florida consisted of 43% of the total ABC implementation cost. This indicates that the safety benefits of ABC implementation consist of a considerable portion of its benefit-cost ratio

A Temporal Investigation of Crash Severity Factors in Worker-Involved Work Zone Crashes: Random Parameters and Machine Learning Approaches

In the context of work zone safety, worker presence and its impact on crash severity has been less explored. Moreover, there is a lack of research on contributing factors by time-of-day. To accomplish this, first a mixed logit model was used to determine statistically significant crash severity contributing factors and their effects. Significant factors in both models included work-zone-specific characteristics and crash-specific characteristics, where environmental characteristics were only significant in the daytime model. In addition, results from parameter transferability test provided evidence that daytime and nighttime crashes need to be modeled separately. Further, to explore the nonlinear relationship between crash severity levels and time-of-day, as well as compare the effects of variables to that of the logit model and assess prediction performance, a Support Vector Machines (SVM) model trained by Cuckoo Search (CS) algorithm was utilized. Opening the SVM black-box, a variable impact analysis was also performed. In addition to the characteristics identified in the logit models, the SVM models also included the impacts of vehicle-level characteristics. The variable impact analysis illustrated that the termination area of the work zone is most critical for both daytime and nighttime crashes, as this location has the highest increase in severe injury likelihood. In summary, results of this study demonstrate that work zone crashes need to be modeled separately by time-of-day with a high level of confidence. Furthermore, results show that the CS-SVM models provide better prediction performance compared to the SVM and logit models

Factors Affecting Injury Severity in Vehicle-Pedestrian Crashes: A Day-of-Week Analysis Using Random Parameter Ordered Response Models and Artificial Neural Networks

The high number of vehicle–pedestrian crashes in the United State has gained increased attention among transportation safety analysts in recent years. Being directly exposed to the collision force makes pedestrians more prone to becoming severely injured when in crash than other road users. Considering the fact that pedestrian-involved crashes is a serious public health problem, the current study’s aim is to investigate the contributing factors associated with injury severity of pedestrian crashes by time-of-week. Separate injury severity models for weekday and weekend crashes were developed, and the overall stability of the model estimates was examined through likelihood ratio tests. For this purpose, random parameter ordered-response models were employed to specify the ordinal nature of injury severity levels and capture the potential unobserved heterogeneity. In addition, Artificial Neural Network (ANN) was used to explore the nonlinear relationship between explanatory variables and severity outcomes. Comparison of the prediction performance demonstrated that optimized ANN provides superior results compared to conventional statistical approaches. A variable impact analysis was then conducted on the optimized ANN to investigate the effects of the explanatory variables on injury severity. The results revealed the factors that are significantly associated with pedestrian fatalities. These findings further provide insights for a better understanding of pedestrian injury severity in weekday vs. weekend crashes through the impact analysis of various explanatory variables

Improved Support Vector Machine Models for Work Zone Crash Injury Severity Prediction and Analysis

Work zones are a high priority issue in the field of road transportation because of their impacts on traffic safety. A better understanding of work zone crashes can help to identify the contributing factors and countermeasures to enhance roadway safety. This study investigates the prediction of work zone crash severity and the contributing factors by employing a parametric approach using the mixed logit modeling framework and a non-parametric machine learning approach using the support vector machine (SVM). The mixed logit model belongs to the class of random parameter models in which the effects of flexible variables across different observations are identified, that is, data heterogeneity is taken into account. The performance of the SVM model is enhanced by applying three metaheuristic algorithms: particle swarm optimization (PSO), harmony search (HS), and the whale optimization algorithm (WOA). Empirical findings indicate that SVM provides higher prediction accuracy and outperforms the mixed logit model. Estimation results reveal key factors that increase the likelihood of severe work zone crashes. Furthermore, the analysis illustrates the ability of the three metaheuristics to enhance the SVM and the superiority of the harmony search algorithm in improving the performance of the SVM model. The occurrence of fatal road crashes has followed an increasing trend in recent years, illustrated by a 32% growth from 2,228 fatal road crashes in 2013 to 2,933 in 2017 in the State of Florida. Florida is among the three states in the U.S.A. with the highest rates of fatality road crashes (1). Miami-Dade County had the highest number of road crashes in Florida, with a total of 33,694 fatality and injury crashes out of 64,070 crashes in 2016 (2). The number of work zones in Florida has also increased, because of the growth of highway renovation and construction projects. As such, the number of crashes associated with work zones has also increased, from 1,153 in 2013 to 1,315 in 2017. Thus, safety should be an important consideration for decision makers, as they plan, design, and operate work zones. Geometric characteristics, traffic control, and smart work zones have significant impacts on the occurrence of work zone crashes. As a result, a better understanding of the contributing factors of work zone crashes can help to identify appropriate countermeasures to improve roadway safety. Work zone crashes constitute approximately 1% of the total crashes in Miami-Dade County. Fatalities occur in just 0.5% of work zone crashes, which is over twice the amount of fatalities in road crashes not involving work zones (i.e., 0.2%). Although the low percentages of this type of crash may not seem alarming at first glance, the significant percentage of loss of life suggests an emergent need for comprehensive and in-depth investigation. Another aspect of work zone crashes is workers’ safety. Approximately 3,400 workers were injured in work zone crashes between 2013 and 2017 in Miami-Dade County. Moreover, considering that around 38% of work zones involve lane closure, the economic impact of travel delay associated with additional lane closures because of incidents can be substantial (3). In light of this, this study investigates the factors that affect the severity levels of work zone crashes using a disaggregate level analytical approach, in which individual crash records and associated potential contributing factors are studied. By applying a mixed logit modeling framework, a parametric approach, the significant contributing factors affecting driver and passenger injury severity at work zones will be investigated first. Next, the authors propose a support vector machine (SVM) modeling framework, a machine learning approach, with multilayer perceptron and Gaussian radius basis function kernels to classify crash records. Three different metaheuristic algorithms are then applied—particle swarm optimization, harmony search, and the recently introduced whale optimization algorithm—to improve the performance of the SVM. The results from the two models are then compared in relation to the contributing factors identified and the prediction performance. The remainder of this paper is organized as follows. The next section summarizes the most recent and relevant studies, in which analytical (i.e., parametric) modeling approaches and machine learning techniques are used to study injury severity. The methodology is then presented, which includes brief descriptions of the methods used. This is followed by brief descriptions of the metaheuristic algorithms employed to improve the prediction performance of the SVM and corresponding performance measurement metrics. Next, the data description and processing procedure are introduced. Finally, the last section recaps the research outcomes based on the results obtained and provides concluding remarks

A temporal investigation of crash severity factors in worker-involved work zone crashes: Random parameters and machine learning approaches

In the context of work zone safety, worker presence and its impact on crash severity has been less explored. Moreover, there is a lack of research on contributing factors by time-of-day. To accomplish this, first a mixed logit model was used to determine statistically significant crash severity contributing factors and their effects. Significant factors in both models included work-zone-specific characteristics and crash-specific characteristics, where environmental characteristics were only significant in the daytime model. In addition, results from parameter transferability test provided evidence that daytime and nighttime crashes need to be modeled separately. Further, to explore the nonlinear relationship between crash severity levels and time-of-day, as well as compare the effects of variables to that of the logit model and assess prediction performance, a Support Vector Machines (SVM) model trained by Cuckoo Search (CS) algorithm was utilized. Opening the SVM black-box, a variable impact analysis was also performed. In addition to the characteristics identified in the logit models, the SVM models also included the impacts of vehicle-level characteristics. The variable impact analysis illustrated that the termination area of the work zone is most critical for both daytime and nighttime crashes, as this location has the highest increase in severe injury likelihood. In summary, results of this study demonstrate that work zone crashes need to be modeled separately by time-of-day with a high level of confidence. Furthermore, results show that the CS-SVM models provide better prediction performance compared to the SVM and logit models

COVID-19 and Injury Severity of Drivers Involved in Run-Off-Road Crashes: Analyzing the Impact of Contributing Factors

This study examined the relationship between the lockdown during the COVID-19 pandemic and the severity of injuries sustained by drivers involved in run-off-road (ROR) crashes. A random parameter ordered logit (RPOL) modeling framework was utilized to account for the ordinal nature of severity outcome and capture the potential unobserved heterogeneity. The data used in this study contained ROR crashes that occurred in the state of Florida from April to September for 2019 and 2020 representing non-pandemic and pandemic time periods, respectively. Separate driver injury severity models were developed across the two time periods, and the overall stability of the model estimates was examined through likelihood ratio tests. The impacts of various potential contributing factors, including crash-, driver-, and vehicle-related variables, roadway geometric characteristics, environmental conditions, and traffic-specific factors, were assessed. Although the developed models share some common features, the analysis results showed that the model specifications indicated a strong temporal instability among the estimated parameters. Compared to the non-pandemic period, the following variables resulted in increased driver injury severity in ROR crashes during the pandemic: drivers 65 years or older, careless driving, and absence of traffic control devices