Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance.

In this research work, clustering techniques were used to characterize crashes occurring on rural and suburban highways in Jordan, while support vector machines were used to compare the classification performance of the resulting clusters with that obtained without clustering, the data used included 10219 crashes obtained from the Jordanian Traffic Institute for (2014–2018). Support vector machines with four kernel functions were used to measure the classification performance using different clustered and non-clustered data. RBF kernel showed a prominent performance in classifying injury severity levels as indicated by the results obtained by the performance measures used (accuracy, precision, recall, and F1-score). It was found that classification performance as measured by support vector machines of the clustered sets was superior to that obtained using the non-clustered set. All clustered models were remarkably able to predict injury severity levels using the RBF kernel with F1-score ranging from 72% to 81%.

Various monotonic cracking resistance assessment tests and indicators of asphalt mixes have their own merits; however, they provide illogical interpretations of mix resistance to cracking under different air void (AV) contents. This study aims to investigate and address the limitations of the monotonic tests and indicators in evaluating the cracking resistance of asphalt mixes with different AV contents. The results show that the shape of the load-displacement curve, curve basic elements, and monotonic indicators are significantly sensitive to variation in AV content. However, the currently proposed correction ratios could not address this dependency of cracking assessment on AV content. This study therefore proposes and evaluates a new approach and correction ratios for monotonic tests and performance indicators. The results demonstrate that the newly proposed correction ratios could normalize the effect of AV content on the examined performance assessment indicators.

This study investigated the performance of rural public bus transport services in Jordan Valley. The performance measures included availability, comfort and convenience, waiting time, mobility, productivity and safety. The data used in this research was collected from three sources: field survey of existing bus routes, operational data from Land Transport Regulatory Commission and questionnaire surveys which were distributed to a sample of passengers and bus drivers. The obtained data was used to compute the performance measures, as well as to develop statistical models to estimate the average waiting time of buses at peak and off-peak hours. The results indicated that the waiting time at off-peak hours was two and a half times the waiting time at peak hours. It was found that 40% of the external bus routes were within the range of overall Level of Service (LOS) C & D, 26.67% between B & D, 13.33% between B & C, 13.33% between C & E and 6.67% between D & E. Also, it was found that 60% of internal bus routes’ LOSs were between C & D, 20% between C & E and 20% with LOS C. It was found that the average perceived waiting time depends on the total travel time regardless of on-peak or off-peak trips. KEYWORDS: Public transport, Bus services, Performance measures, Waiting time, Jordan Valley.