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.
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.
With Dubai aiming to be one of the greenest cities in the world, the local government has set various requirements and regulations to achieve this goal. The Dubai Municipality has already established and implemented its own locally-developed green building regulation, Green Building Regulations & Specifications (GBR&S), to address major concerns associated with buildings’ resource efficiency. This study proposes a comparison between the GBR&S system with USGBC’s LEED v4 rating to identify where GBR&S falls short and how much it would cost to upgrade to the various levels of LEED certification. The authors also hypothesize that access to efficient, mass public transportation systems is a major factor when determining the cost of LEED certification. With this in mind, proximity to a public transportation network and its effect on the cost of a LEED upgrade were investigated.
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