Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques

Hazaymeh, Khaled; Almagbile, Ali; Alomari, Ahmad H.

doi:10.3390/ijgi11040260

Cited by 34 publications

(20 citation statements)

References 33 publications

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“…The study conducted by Hazaymeh et al [29] investigated the temporal and spatial patterns of traffic accidents in Irbid Governorate over a period of five years, from 2015 to 2019, based on available data. The study utilized the Global Moran I index and hotspot analysis (Getis-Ord G i *) techniques in ArcGIS to identify the road accident hotspots.…”

Section: Road Accident Hotspotsmentioning

confidence: 99%

“…Despite the fact that the aforementioned studies drew attention to clusters of road accidents, they did not conduct statistical significance tests to analyze their spatial distribution. In this context, very few studies have attempted to comprehend the spatial distribution of road accidents in Jordan, even though there is a great deal of accident data [28,29]. In many previous studies, road accident counts were frequently used to assess a position's safety issues [25,30,31].…”

Section: Introductionmentioning

confidence: 99%

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Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

et al. 2023

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One of the primary objectives of transportation engineering is to increase the safety of road infrastructure. This study seeks to determine the relationship between geometric design parameters in relation to road accident criteria based on accident hotspots on Jordan’s Desert Highway. The road accident data (from 2016 to 2019) were collected from the Jordan Traffic Department. The spatial pattern of hotspots was identified using a GIS tool named Getis-Ord Gi* based on the severity index of road accidents. A topographic survey was conducted to investigate the road alignment and intersections at hotspot locations. The study utilized the Structural Equation Modeling (SEM) technique via SmartPLS to highlight the correlation between geometric designs in relation to road accidents. The hotspot analysis (Gits-Ord Gi) discovered 80 road accident hotspots along the highway. The study found that horizontal alignment and road intersections significantly impact road accidents in hotspot locations. Furthermore, vertical alignment has no effect on road accidents in hotspot areas. The study enhanced the comprehension of the factors associated with road geometrics and intersections that affect the occurrence of road accidents.

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Section: Road Accident Hotspotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

et al. 2023

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“…The spatial distribution patterns of traffic accidents in the Al-Ahsa Region were identified using spatial autocorrelation (Global Moran's I). If the characteristics are spatially clustered, dispersed, or randomly distributed, it is shown by the values of Moran's I [84]. Equation ( 14) was used to calculate the Global Moran's I, z-score, and p-value using the spatial autocorrelation…”

Section: Spatial Autocorrelation and Hotspot Analysismentioning

confidence: 99%

“…where X i is an attribute value of the target feature at location i, N is the total number of features, W i,j is the spatial weight between features at locations i and j, and the neighboring feature at locations i and j has an attribute value of X j [84]. The results of Moran's I statistic can be verified using z scores, where a confidence level is established.…”

Section: Spatial Autocorrelation and Hotspot Analysismentioning

confidence: 99%

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

et al. 2022

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The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.

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“…Finally, the presence of local spatial association was tested within the analysed data, for clustering geographically the hotspots, by applying the Getis-Ord Gi* statistic [31]. The Getis-Ord Gi* statistic is a tool of local spatial autocorrelation analysis, used in several contexts, such as cluster regions in the transportation equipment industry [32], forest fire management [33] active school travel (i.e., walking) clusters [34], industrial clusters [35], road accidents analysis [36,37].…”

Section: Nature-based Attractive Clustersmentioning

confidence: 99%

Understanding the Role of Nature in Urban-Rural Linkages: Identifying the Potential Role of Rural Nature-Based Attractive Clusters That Serve Human Well-Being

et al. 2022

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Rural areas provide unique amenities for recreational purposes which are highly appreciated by urban inhabitants. This generates an important but often hidden relationship between the urban and the rural. The aim of our study is first to provide empirical evidence for this linkage and then to identify for Italy, at the municipal level, those rural areas which actually function as nature-based attractive clusters. We used the data coming from a participatory webGIS survey that asked 1632 Italian respondents to mark attractive nature related places locally, regionally, nationally and world-wide to explain quantitatively and qualitatively the relationship between urban and rural. From the survey, indicators were developed to rank the nature-based attractive clusters. Our results pointed out a major (almost double) flow from urban to rural for natural amenities, which increased with the spatial level at which attractive nature areas were marked. This analysis allowed for the identification rural clusters of Italian municipalities that form nodal points for nature-based urban well-being; shedding light on an often neglected urban-rural relationship. The method is applicable in other countries and may stimulate better planning and management strategies for improving rural areas, taking an urban-rural perspective.

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Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques

Cited by 34 publications

References 33 publications

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

Understanding the Role of Nature in Urban-Rural Linkages: Identifying the Potential Role of Rural Nature-Based Attractive Clusters That Serve Human Well-Being

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