Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predicting injury severity; however, these studies did not consider the consequences (cost) of incorrect predictions. This paper addresses this shortfall by considering the monetary cost of incorrect injury severity predictions when developing C4.5, instance-based (IB), and random forest (RF) machine-learning models. One model of each method was developed for four distinct cohorts of drivers (i.e., younger males, younger females, older males, and older females). Each model considered a selection of driver, vehicular, road/traffic, environmental, and crash parameters for determining if they significantly influenced driver injury severity. A five-year period of two-vehicle crash data collected at signalized intersections in the metropolitan area of Miami, Florida was used in the models. Results indicated that cost-sensitive learning classifiers were superior to regular classifiers at accurately predicting injuries and fatalities of crashes. Among cost-sensitive models, RF outperformed C4.5 and IB models in predicting driver injury severity for four groups of drivers. The models displayed substantial differences in injury severity determinants across the age/gender cohorts.

Roadway accidents claim more than 30,000 lives each year in the United States, and they continue adversely affecting people's well-being. This problem becomes even more challenging when aging populations are considered due to their vulnerability to accidents. This is especially a major concern in Florida since the accident risk is increasing proportionally to the population growth of aging Floridians. This study investigates the spatial and temporal patterns of aging people-involved accidents using geographical information systems (GIS)-based methods via a case study of three urban counties in Florida, selected based on their high aging-involved accident rates. A series of spatial analytic methods are utilized to explore accident patterns, including a network distance-based kernel density estimation method, which provides an unbiased distribution of the accidents over the local roadways. An accident density ratio measure is also developed in order to understand how accidents involving aging people occur at different locations than those of the general population. Results indicate that high risk locations for aging-involved accidents show different spatial and temporal patterns than those for other age groups. Investigating these distinct patterns at a high spatio-temporal scale can lead to better aging-focused transportation plans and policies.

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Computerized intelligent systems can simulate human expertise, as well as analyze and process vast amounts of data instantly. This paper presents a hybrid intelligent computerized model for constructed surfaces quality assessment. The assessment of steel bridge coating is used to exemplify the model. The model will automate the assessment process by using computers to analyze digital images of the areas to he assessed in order to identify and measure defect patterns. Most techniques, currently used in construction and infrastructure assessment and quality inspection, rely merely on subjective criteria. Such subjective, and hence inconsistent, assessment techniques have been identified as a critical obstacle to effective infrastructure or constructed facilities management.

In the aftermath of disasters, evacuating aging victims and maintaining an optimal flow of critical resources in order to serve their needs becomes problematic, especially for Gulf Coast states in the USA such as Florida, where more than 6.9 million (36.9%) of the population are over age 50. Scanning the literature, there is no substantial prior work that has synthesized the requirements for a multi-modal emergency needs assessment that could facilitate the safe and accessible evacuation of aging people, and optimize the flow of resources into the affected region to satisfy the needs of those who remain. This paper presents a review of the aging populationfocused emergency literature utilizing a knowledge base development methodology supported with a geographic information system-based case study application set in Florida. Importance is given to both ensuring the resiliency of the transportation infrastructure and meeting the needs of aging populations. As a result of this metadata-based analysis, critical research needs and challenges are presented with planning recommendations and future research directions. Results clearly indicate that transportation agencies should focus on clear and fast dissemination of disaster-related information to the aging populations. The use of paratransit services for evacuating aging people, especially those living independently and/or in rural areas, is also found to be of paramount importance.

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Gap acceptance predictability has become a vital area of interest for traffic safety and operations due to its complexity and significance in understanding a population’s driving behavior. Recent studies have implemented statistical modeling techniques, such as binary logit model (BLM), to predict drivers’ gap acceptance behaviors. However, these models have inherent presumptions and pre-set correlations that, if contravened, can produce erroneous results. The use of non-parametric data mining techniques, such as decision trees, avoids these deficiencies, thus resulting in improvements to the predictive capability of the models. In this study, the feasibility of C4.5 decision trees, instance-based (IB), and random forest (RF) models for predicting drivers’ gap decisions was examined by comparing their results with BLM. To accomplish this objective, 66 study participants drove through ten driving simulation scenarios requiring the navigation of unprotected right and left-turning maneuvers at four-legged, signalized intersections. The data collected from these tests will provide means to directly compare and rank the data mining and statistical models, while also allowing for the identification of variables that are significantly influencing gap acceptance. Results produced from the models indicated that data mining models were superior to BLM at accurately predicting a participant’s gap decisions. RF models outperformed the C4.5 and IB models in predicting gap acceptance behaviors for both the left and right turning scenarios. Because of its superior performance, the authors recommend the implementation of the RF model for predicting gap decisions at unprotected signalized intersections.