In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. The vehicles involved in those crashes were not equipped with any crash avoidance systems. We then used the accident reconstruction method to build those crash scenarios in PC-Crash software. Then, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. The range and distribution of input parameters were extracted from the related field crash data and available literature. A total number of 16000 simulations have been conducted which produced input-output datasets for further investigations. Finally, the effects of input parameters on simulation outcomes including crash occurrence, AEB activation, injury risk, and vehicle damage have been quantified using the Boruta algorithm. The results indicated that the overall effectiveness of the AEB system was a 57% reduction of rear-end crashes, a 52% reduction of injury severity (striking vehicle’s passengers), and a 47% reduction of damages for striking vehicles. The results also showed that the available AEB algorithms were more effective for the average speed equal to or less than 80 kmph. The speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. In addition, the results indicated that the performance of FCW had a direct impact on the effectiveness of the AEB system for the integrated FCW + AEB system.

Hurricanes affect millions of people in the U.S. every year and cause billion-dollar economic losses. Florida is one of the states in the U.S. which is vulnerable to hurricanes and major infrastructure damages were reported due to these extreme events such as Hurricane Hermine (2016). This study aims to assess the impacts of Hermine on the transportation network of Tallahassee, the capital of Florida, using a spatial-analytical approach and real-world hurricane data on roadway closures, power outages, power network configuration, and other storm-related variables. First, a geographic information systems (GIS)-based analysis was utilised to obtain the spatial patterns of hurricane-induced roadway closures along with power outages, power lines, downed trees, and the city’s flood hazard zones. Second, a statistical model was developed to understand the association between the aforementioned variables and roadway closures. The spatial model results revealed that the roadway closures and power outages almost have the same patterns, and the city centre experienced more disruptions than other areas. The statistical model findings indicated that the power outages and power lines have strong relationships with roadway closures. The obtained knowledge of this study can provide valuable information for city officials to prepare better emergency policies for future potential hurricanes.

This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the nonlinear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement outlier scenarios. A case study involving a Global Positioning System based localization in an urban environment and data containing multipath problems demonstrates the application of the proposed technique.

Due to uncertainties in material properties and use conditions, reliability predictions are often subject to considerable error. Such inaccurate predictions lead to maintenance decisions that are expensive or not able to prevent failures. This article proposes a Bayesian approach for failure time prediction of degrading components from condition data that accounts for uncertainties and incorporates prior information on degradation behavior via prior distributions. The approach is based on posterior sampling to handle more general statistical models. Simulation examples are presented to show that by incorporating condition monitoring data or including prior knowledge the effectiveness of maintenance decisions can be significantly improved. Application of the approach in a real setting is illustrated using data from the literature.

Florida has distinctively different regions, in terms of population density and hurricane risk. The objective of the article is to understand the characteristics of hurricane loss in different regions of Florida. Two different structures with different components capacities were taken into consideration. The first structure does not meet the current building standards in terms of the high wind performance (‘weak’) and the second structure is built from hurricane proved materials approved by Florida Building Code (‘strong’). In order to bring a new insight into the regional differences in hurricane loss, Exceedance Probability of Loss (EPL) is introduced. The EPL is the probability that the total annual loss will exceed a certain level. The results of this research identified the most vulnerable parts of Florida in terms of the EPL. It has been shown how to combine results of analyses conducted with percentage loss and dollar loss to arrive at a fuller understanding in regional analyses. Finally, the paper also discussed the importance of roof-to-wall connections in preventing the roof uplift.