This paper aims to explore the spatiotemporal pattern of traffic accidents using five years of data between 2015 and 2019 for the Irbid Governorate, Jordan. The spatial pattern of traffic-accident hotspots and their temporal evolution were identified along the internal and arterial roads network in the study area using spatial autocorrelation (Global Moran I index) and local hotspot analysis (Getis–Ord Gi*) techniques within the GIS environment. The study showed a gradual increase in the reported traffic accidents of approximately 38% at the year level. The analysis of traffic accidents at the severity level showed a distinguished spatial distribution of hotspot locations. The less severe traffic accidents (~95%) occurred on the internal road network in the Irbid Governorate’s towns where the highest traffic volume exist. The spatial autocorrelation analysis and the Getis–Ord Gi* statistics with 99% of significance level showed clustering patterns of traffic accidents along the internal and the arterial road network segments. Between 2015 and 2019, a notable evolution of the traffic-accident hotspots clusters was pronounced. The results can be used to guide traffic managers and decision makers to take appropriate actions for enhancing the hotspot locations and improving their traffic safety status.

This study explores the strategic integration of Building Information Modeling (BIM) and Virtual Reality (VR) within the built environment, addressing the growing complexities of urban development. Through a detailed examination of BIM applications, the research highlights a 37 % reduction in design conflicts and iterations, thereby enhancing design precision and efficiency in urban construction projects. Simultaneously, VR technology is demonstrated to increase stakeholder engagement by 62 % and improve spatial awareness by 48 %, fostering greater community participation and inclusivity in the development process. The combined use of BIM and VR optimizes not only construction workflows but also considerably enhances environmental and socio-economic outcomes, such as a remarkable 20 % reduction in greenhouse gas emissions. It will, therefore, support advanced urban infrastructure development and provide a replicable model for sustainable and resilient urban planning. This research underlines the crucial contribution of integrating BIM and VR technologies to the development of sustainability and resilience of the built environment.

This study describes crash causes, conditions, and distribution of accident hot spots along with an analysis of the risk factors that significantly affect severity levels of crashes and their effects on pedestrian safety using machine learning (ML) techniques. Supervised ML algorithm–random forest and decision tree–based algorithm-AdaBoost algorithms are applied and compared to predict the severity level and future crashes based on road crash elements. Association rule, an unsupervised learning algorithm, is utilized to understand the association between driver characteristics, geometric elements of the highway, the environment, time, weather, and speed. Slight, medium, and severe injuries and fatalities in crashes are also considered to understand the behavior of road drivers, who are most likely to cause crashes. Fatalities and injuries are studied with spatial statistics analysis. The variables most affecting the severity of the crash are determined and discussed in detail. The results are checked for accuracy, sensitivity, specificity, recall, precision, and F1 score performance. The impact of drivers, vehicles, and road characteristics is investigated in traffic crashes. The random forest model was found to be the most suitable algorithm to predict crash severity levels.

The present study developed Multiple Linear Regression (MLR) and machine learning (ML) models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF), to predict the mean free-flow speed (FFS) using several geometric, traffic, and pavement condition variables. The traffic features group includes spot speed, speed limit, average speed, 85th percentile speed, traffic and crossing pedestrian volumes, volume of exiting vehicles, percentage of elderly crossing pedestrians (Elderly%), percentage of heavy vehicles (HV%), and traffic calming measures (TCMs). The geometric characteristics include lateral clearance, number of effective lanes, number of access points (including median openings), road grade, effective lane width, and median width. The pavement condition category includes pavement roughness in the International Roughness Index (IRI). A total of 11 urban arterials were used to develop the MLR model and train the ML models. Test data were collected from two randomly selected roads to evaluate the performance of each model, investigate the differences between conventional linear regression and ML approaches, and determine the best prediction models based on the results of the two techniques. Results showed that the proposed ML algorithms outperformed linear regression models. They are believed to be valuable and strong tools to predict the mean FFS that adapts to sudden changes in traffic flow caused by exogenous conditions on urban arterials and can be employed in determining the most influential factors and building reliable prediction models where spot study is not feasible due to time and resource limitations.

Transit Signal Priority (TSP) and Bus Rapid Transit (BRT) are innovative Intelligent Transportation System (ITS) tools that can reduce travel times for buses. Combining TSP and BRT can significantly improve bus travel, but can negatively impact network traffic operations. Although TSP has been implemented worldwide, few previous studies holistically examined the effects of using various conditional and unconditional TSP strategies with or without a BRT system. This research simulates multiple TSP and BRT combination scenarios to understand their impact on traffic operations, including crossing street traffic. A test bed along International Drive (I-Drive) in Orlando, Florida, was chosen as the simulation area. Field data collected for this test bed, which included traffic volumes, bus travel times, and traffic signal control data, were used to develop, calibrate, and validate the simulation model. Results showed that BRT with Conditional TSP 3 minutes behind significantly improved travel times, average speed, and average total delay per vehicle for the main through movements compared with no BRT or TSP, with only minor effects on crossing street delays. BRT with Unconditional TSP resulted in significant crossing street delays, especially at major intersections with high traffic demand, indicating that this scenario is impractical for implementation. The simulation suggests that BRT and TSP will be most effective when used in areas where crossing street volumes are low. However, it is unknown how these ITS tools affect pedestrian traffic. Using optimization methods can determine the best strategy to balance transit and pedestrian traffic.