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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.

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.

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Understanding and dealing with safety aspects of crowd dynamics in mass gatherings of people related to sports, religious and cultural activities is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from the Kingdom of Saudi Arabia hosting millions of pilgrims each year during the Hajj and Omrah seasons suggest that stampedes in mass gatherings occur frequently and highlight the importance of studying and dealing with the crowd dynamics more scientifically. In this regard, efficient monitoring and other safe crowd management techniques have been used to minimize the risks associated with such mass gathering. An example of these techniques is real-time monitoring of crowd using a UAV (Unmanned Aerial Vehicle); this technique is becoming increasingly popular with the objective to save human lives, preserve environment, protect property, keep the peace, and uphold governmental authority. In this paper, a crowd monitoring system for pedestrians has been proposed and tested. The system has deployed crowd monitoring technique using real-time images taken by UAVs; the collected data was investigated, and crowd density was estimated using image segmentation procedures. A color-based segmentation method has been employed to detect, identify and map crowd density under different camera positions and orientations. Furthermore, the associated anomalies/outliers which may lead to non-classification of features have been eliminated using image enhancement tools. The paper presents a crowd monitoring system for pedestrians that can contribute to an area of research still in its infancy. The proposed system is a valuable tool in terms of facilitating timely decisions, based on highly accurate information. The results show that the used image segmentation technique has the capability of mapping the crowd density with an accuracy level up to 80%.