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(a) location map of the Irbid Governorate in Jordan and (b) administrative boundaries of the 18 municipalities in the Governorate.
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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 (Glob...
Contexts in source publication
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... terms of population, it is the second-largest governorate in Jordan after Amman, with approximately 1,957,000 inhabitants in 2019 (the highest population density in the country is at ~1178.7 inhabitants/km 2 ). It consists of 18 municipalities ( Figure 1). The governorate witnessed progressive urban and economic growth after 1990 for various reasons, including natural population growth and external immigration from other cities and neighboring countries. ...
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... instance, fatal accidents occur on arterial roads due to the high-speed limit of approximately 100 km/h. Figure 10a shows a section of the arterial road that links Irbid city with the capital, Amman. It witnesses high traffic and pedestrian volume as it is surrounded by various residential, educational, and commercial activities. ...
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... second example (Figure 10b) represents one of the major sections in Irbid city. It has very high traffic and pedestrian volumes as it is surrounded by numerous commercial stores with different activities, residential households, governmental buildings, and Yarmouk University. ...
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... addition, it is recommended to allocate specific parking lots and bus stops to reduce random parking of cars and public transit. Figure 10c shows a section of the arterial road that links Irbid city with the towns in the Jordan Valley. This section has a high speed limit of 80km/hour with a gentle gradient and sharp and semi-sharp curves and road junctions and bridge loops. ...
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... mistakes can be mitigated by setting traffic speed cameras. Figure 10d shows a section of the Jordan Valley Road that receives high traffic and pedestrian volumes. The main reasons for traffic accidents refer to drivers' mistakes such as exceeding speed limits, driving in the opposite lane, and crossing the traffic light. ...
Citations
... Despite the increasing utilization of GIS for identifying road accident hotspots for identifying road accident hotspots (Munasinghe, 2023), there remains a notable gap in the research literature pertaining to the specific context of Jordan (Hazaymeh et al., 2022). While studies from various regions have demonstrated the efficacy of GIS in pinpointing accident-prone areas, the unique geographic, demographic, and infrastructural characteristics of Jordan necessitate a tailored approach (Maaiah et al., 2021). ...
High numbers of road accidents at Jordan's major highways pose a substantial threat to public safety, necessitating strategic road safety interventions. This study presents hotspot spatial analysis based on severity indices for three prominent highways in Jordan via geographic information system (GIS) software. A comprehensive road accident network was constructed based on Jordanian road accident data (locations: Highways 30, 35, and 10) from year 2016 to 2019. Each incident's severity index was taken into consideration. Hotspot patterns were identified using GIS tools namely (a) Getis-Ord Gi* statistic and (b) Global Moran I index for spatial autocorrelation analysis, as they provide detailed information about the spatial distribution as well as statistical significance measures for road accident hotspots. The results revealed critical insights into the distribution of accident hotspots along the selected highways. In ascending order, the statistically significant hotspots observed were 14, 17, and 78 hotspots from Highways 10, 30, and 35. Hotspot patterns can be classified as random (Highway 30), dense (Highway 10), and dense clusters (Highway 35). Highway 35 demands targeted interventions to mitigate accidents along this route. This research takes away the hotspots along the highways that can be a good reference for stakeholders (e.g., transportation planners, policymakers, and relevant authorities) to implement best practices and road safety measures on Jordan's major highways.
... With the rapid development of urban and commercial sectors, transportation infrastructures have also increased, leading to an upsurge in road traffic accidents. Several geospatial studies showed road traffic accidents to be caused by various factors, such as weather conditions that cause slippery roads [2], the absence of proper road authorities and the management of necessary safety procedures [3,4], and low investment in safety precautions [5]. Additionally, human elements, such as the reckless driving behavior of young drivers and pedestrian negligence, were identified as significant contributors to road traffic accidents [6]. ...
... Geographic Information Systems (GISs) have become a popular tool for observing and analyzing worldwide road traffic accidents over the past few decades [5,9,10]. Having the ability to store, analyze, and manage vast amounts of data, GISs are used to visually explore the relationships between accidents and contributing factors from a spatial and non-spatial perspective [11]. ...
... This method interprets the spatial clustering of high values as a positive value, whereas negative values indicate spatial clustering of low values [54]. This is yet again a commonly used hotspot identification tool used by many researchers [5,20,36,49,50,55]. Utilizing KDE alongside the Getis-Ord Gi* technique provides insight into how the population density correlates with accident hotspots. ...
Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.
... Hazaymeh et al. investigated the spatiotemporal patterns of car accidents over several years in Jordan [9]. They used a GIS-assisted technique based on statistical and clustering approaches to identify areas with car crash points. ...
This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technology, MTSA-related accidents were identified on maritime routes near both assessed and unassessed project sites from 2010 to 2014. Subsequently, by applying the synthetic minority oversampling technique to balance the data, the algorithm learned from the random forest using the operational data of coastal passenger ship operations and accident data near unassessed locations where MTSA is not implemented. Then, the trained model was applied to predict accident occurrence in the absence of MTSA near the latest operational information of coastal passenger ship operations at the assessed project sites. The MTSA-related ARR was then calculated by applying the actual accident occurrences during operation near the assessed project sites where MTSA was implemented. The MTSA-marine ARR calculated at 17.41% can be applied to the calculation of safety benefit for MTSA. The results of this study can provide quantitative evidence for the application of higher-level systems considering the burden on regulatory targets when improving MTSA or similar systems.
... Cheng et al. proposed a comprehensive spatiotemporal analysis method integrating time-space cube analysis, spatial autocorrelation analysis, and emerging hotspot analysis to investigate the characteristics of traffic accident development and identify accident hotspots in Wujiang [19]. Hotspot analysis (Getis-Ord G i ⁎ ) techniques within the GIS environment employed in determination of the spatio-temporal pattern of traffic accidents have been used in various traffic analyses as indicated by Hazaymeh et al. for Irbid Governorate, Jordan [20]. In the study "Spatio-Temporal Clustering of Road Accidents: GIS-Based Analysis and Assessment," Prasannakumara et al. used spatial clustering of accidents and spatial density of hotspots conducted according to Getis-Ord G i ⁎ statistics [21]. ...
The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.
... GIS techniques can be applied to find hotspots, which can be further categorized as emerging, sporadic, persistent, consecutive, intensifying or diminishing depending on their characteristics [9]. Identification of such hotspots is vital for road planners to reduce the number of potential road accidents [3,[10][11][12]. ...
Road accidents are a major source of trauma worldwide due to increasing numbers of vehicles and drivers. Considerable data has been collected on road accidents and this is frequently published as open data on the internet. These datasets include parameters such as accident type and number of fatalities as well as environmental variables such as road type, demographics, and area infrastructure. Geospatial analysis provides a means of understanding spatial factors that influence road accidents such as the built infrastructure, natural environment features such as hills and vegetation, traffic volume, and road design and construction. Geospatial visualization techniques can also help identify hotspots and blackspots. The Moran I, Getis-Ord, and Kernel Density Estimation techniques are the most commonly-used geospatial tests and their application is reported in many papers. This paper provides a review of geospatial factors that are relevant to road accidents.
... Through the examination of the spatial and temporal patterns of accidents, it is feasible to pinpoint places with a higher likelihood of accidents, sometimes referred to as hotspots (Soltani and Askari, 2017;Soltani and Askari, 2014). Subsequently, this data can be utilized to allocate resources in a prioritized manner and execute focused actions aimed at enhancing road safety (Hazaymeh et al., 2022). Moreover, the examination of spatial patterns might unveil the impact of several spatial elements, such as road attributes and land use, on the frequency of accidents (Harirforoush et al., 2019). ...
The focus of this paper is to analyze the trends and locations of accidents in the Greater Melbourne Area (GMA)during a 15-year period (2006–2020). The places where accidents were most prevalent were discovered and the reasons which are contributing to the high accident rates in those areas are determined. Analyzing the patterns over time and variations in the frequency of accidents helped to identify areas that have improved or deteriorated in terms of road safety. A Tweedie model was developed to examine the intricate interaction between the accident frequency and its potential contributing factors such as socio-demographics, road transport infrastructure, and the built environment. Ultimately, a clustering analysis was performed to elucidate the dispersion of road accident risk ratings among different local government areas (LGAs), offering useful insights into road safety initiatives and prioritization.
... But, road crashes are more prevalent in developing countries and despite the only accounting for one-third of the whole worldwide fleet of vehicles, developing countries account for 80% of traffic fatalities (Banik, Chowdhury, Hossain, & Mojumdar, 2011). Several studies have shown that countries with low and moderate incomes have the highest risks of injury and death in road traffic because of rapid and unplanned urbanization, intensive economic growth, poor road maintenance (Achu, Aju, Suresh, Manoharan, & Reghunath, 2019;Hazaymeh, Almagbile, & Alomari, 2022;Rahman, Crawford, & Schmidlin, 2018;Ruikar, 2013;Yalcin & Duzgun, 2015), lack of adequate infrastructure and lack of legal frameworks to ensure their regularity (Yalcin & Duzgun, 2015). Road traffic accidents are estimated to cost most countries 3% of their gross domestic product (GDP) each year (WHO, 2022). ...
... However, the location and timing of intervention operations can be determined by identifying the spatiotemporal distribution patterns of traffic accidents and their hotspots areas (Harirforoush, Bellalite, & Bénié, 2019). Few studies have taken into account both the spatial and temporal features of car accident data (Hazaymeh et al., 2022;Kang, Cho, & Son, 2018;Kilamanu, Xia, & Caulfield, 2011;Laila Achu et al., 2019;Mahata et al., 2019;Medrano & Aznarte, 2020;Rodríguez-morales et al., 2013). ...
We have proposed a data-driven method for spatio-temporal analysis of car crashes based on the Multi Criteria Decision Making (MCDM) procedure in the Zanjan city, NW Iran which are recorded in the period 2019-2020. A combination of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used in this paper to accurately identify the spatio-temporal interactions exist in car crashes. A data-driven AHP-TOPSIS procedure is arranged based on assigning proper weights to the time series related to the peak of accidents. On the other hand, for spatial analysis, the Kernel Density Estimation method was used to create the continuous-value maps of different traffic accidents and then classified using Natural Breaks Classifier. In fact, the proposed methodology can be used to identify car crash hotspots by considering spatio-temporal interactions as well as addressing exaggerated weightings arising from knowledge-driven modeling. By using the spatio-temporal interaction maps in which the location and time of crashes are considered, simultaneously, it is possible to provide a new scientific strategy for identifying car crash hotspots which can lead to better traffic management, improved allocation of resources, and enhanced prevention regulations.
... Moreover, a GIS is a potential approach for spatial analysis to determine the accident hotspots. Identifying the locations of highfrequency accidents, or "hotspots", is one of the most crucial tasks in the effort to decrease the number of traffic accidents [25][26][27][28]. The locations of traffic accident hotspots can be determined via the locations of accident clusters [13,29]. ...
Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.
... Hotspots are caused by the geographic order or geographical location of high-value objects (Hazaymeh et al., 2022). Kernel density estimation is a common method in hotspot analysis (Han et al., 2023;Wang et al., 2010). ...
Timely understanding of affected areas during disasters is essential for the implementation of emergency response activities. As one of the low-cost and information-rich volunteer geographic information, social media data can reflect geographic events through human behavior, which is a powerful supplementary source for fine-grained flood monitoring in urban areas. However, the value of social media data has not been fully exploited as potential location and water depth information may be embedded in both text and images. In this study, we propose a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas based on Sina Weibo. First, we construct a novel fine-grained location corpus specifically for urban flooding contexts. The corpus summarizes characteristics of address descriptions in flood-related Weibo texts, including standard address entities and spatial relationship entities, based on the named entity recognition (NER) model. Then, water depth information in texts and images is obtained based on different deep learning modules and fused at the decision level. Specifically, in text analysis module, we summarize and extract diverse descriptions of water depth, and in image analysis module, we develop a water level hierarchical mapping method. Finally, we analyze the spatio-temporal distribution characteristics and variation patterns of the extracted information to enhance situational awareness. Taking the urban flood occurred in Anhui, China as a case study, we find that the variation of flooding hotspot areas in Sina Weibo and rainfall centers show a significant spatial and temporal consistency, and the fusion of text and image-based information can facilitate dynamic perception of flood processes. The framework presented in this study provides a feasible way to implement refined situational awareness and spatio-temporal evolution analysis of urban floods at the city level in time.
... Traffic crashes are usually abstracted as point data with time and space properties. Previous research has indicated that traffic crashes present certain spatiotemporal aggregation (Hazaymeh, Almagbile, and Alomari 2022). The meaning of the traffic crash hot spot is that an area and its surrounding areas are areas with higher crash rates. ...
