Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (case study: Ilam Province, Iran)

Aghajani, M. A.; Dezfoulian, Reza Shahni; Arjroody, Abdolreza Rezaee; Rezaei, Mohammad Reza

doi:10.1016/j.trpro.2017.05.409

Cited by 68 publications

(33 citation statements)

References 6 publications

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“…The study uses cluster and outlier analysis (the local Moran's I, as discussed in Section 2.3.3) from geo-processing tools in ArcGIS [44,45], by taking the following input parameters, as shown in Table 3, to calculate local Moran's I index, z-score, and p-value for each linear road to obtain road clusters and outliers across Polk County. All roads should have at least one neighbor [32] according to the best practice guidelines of cluster and outlier analysis.…”

Section: The Results Of Road Cluster and Outlier Analysis Of Polk Countymentioning

confidence: 99%

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

2019

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This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road and crash–road spatial relationships. The spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road–road spatial relationships. The conceptualization of crash–road spatial relationships is established using crash spatial aggregation algorithm. The third step, spatial data mining, is to identify RCHC using the cluster and outlier analysis (local Moran’s I index). This approach was validated using spatial data set including roads and road-related crashes (2008–2018) from Polk County, IOWA, U.S.A. The findings of this research show that the proposed approach is successful in identifying RCHC and road outliers.

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Section: The Results Of Road Cluster and Outlier Analysis Of Polk Countymentioning

confidence: 99%

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

2019

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“…Aghajani et al [9] studied spatial and temporal patterns of traffic incidences in Ilam Province located in west of Iran. Their study indicated the cluster distribution of events.…”

Section: Studies Performed In Local Contextmentioning

confidence: 99%

Geographical distribution of at fault drivers involved in fatal traffic collisions in Tehran, Iran

et al. 2020

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OBJECTIVES: According to Traffic Police, about 35% of deaths and more than 50% of injuries caused by traffic collision in the roads of Tehran are among drivers and car occupants. This study was conducted to determine areas with the highest number of traffic collisions and perform spatial analysis of traffic collisions involving drivers in Tehran during April 2014 to March 2017.METHODS: The present study was a cross-sectional and descriptive-analytic research. In this study, all traffic collision that driver was accounted (100 percent or less) for crash occurrence which resulted in the death of at least one person (driver, pedestrian or passenger) were included in the analyses. Geographic information system software was used to show spatial distribution of events from zoning maps. Moran index was used in the mathematical analysis in order to determine the distribution pattern of the events from and Getis-Ord G statistics was applied to analyze the hot spots (high risk points).RESULTS: A total number of 519 traffic collisions were investigated in this study. Moreover, 283 cases (54.5%) of the incidents took place in direct routes and 236 cases (45.5%) occurred at intersections. The most frequent events were in the region 4 (57 cases) and the least frequent events were reported in the region 10 (6 cases). Moran statistics show that the distribution of the studied events significantly follows the cluster pattern (p<0.001).CONCLUSIONS: The northeastern and northwest margins of Tehran are the most prone areas for drivers involved with traffic collisions leading to death. Most traffic collisions leading to death take place at highways located at the entrance and exit points of Tehran and highways in regions 2 and 5.

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“…Road traffic accidents (RTAs) are one of the serious problems at the global level that results in loss of life and economic damage. Road accident fatalities and injuries are increasing all over the world (Aghajani et al, 2017). Due to RTAs about 1.3 million people lose their life, between 20-50 million people disabled and about 518 billion US$ lost annually at global level (Hordofa et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The RTAs are increasing at global level due to the development of transportation infrastructure fails to keep pace with other sectors (Shafabakhsh et al, 2017). The road accidents are mostly the results of three factors, namely, human factors, vehicle factors, and external factors like road conditions (Aghajani et al, 2017;Soehodho, 2017). Another study conducted by Gicquel et al (2017) identifies the use cell phone while driving, and attention deficit disorder with or without hyperactivity is another risk factors for traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

“…Various authors have been used Geographic information science (GIS) technology (Deepthi and Ganeshkumar, 2010; Qian et al, 2012; Gomes, 2012; Dereli and Erdogan, 2017, Aghajani et al, 2017) for spatial and temporal patterns of RTAs identification. The GIS technology has the capacity to indicate the precise location of the traffic accident data, which can be determined by the coordination of the scene of the accident (Dereli and Erdogan, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Identifying Road Traffic Accidents Hotspots Areas Using GIS in Ethiopia: A Case Study of Hosanna Town

Hayidso

Gemeda

Abraham

2019

Transport and Telecommunication Journal

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Due to increasing human population and the number of vehicles, road traffic accident has significant influence on human life and economic development. In the present study, road traffic accident data of three years (2015-2017) were obtained from Hosanna Town Traffic Police Department, and Hosanna Town Transport Authority in Ethiopia. The Global Position System was used to know X, Y coordinates of the accident locations. Global Position System point data and accident data were added to road network data using the ‘Joins and relates’ function in ArcGIS. The results of the study showed a total of 241 Road traffic accidents (RTAs) were occurred in the town from which about 208 victims occurred on people and 33 damaged properties. Based on severity and frequencies of RTAs top nine hot spot areas were identified which requires high attention to protect people and property from damage and loss. Thus, the government and other concerned stakeholders should provide public education and awareness creation to reduce risk of fatalities and property damage due to RTAs in Hosanna town.

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Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (case study: Ilam Province, Iran)

Cited by 68 publications

References 6 publications

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

Geographical distribution of at fault drivers involved in fatal traffic collisions in Tehran, Iran

Identifying Road Traffic Accidents Hotspots Areas Using GIS in Ethiopia: A Case Study of Hosanna Town

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