Investigation of Spatiotemporal Changes in the Incidence of Traffic Accidents in Kahramanmaraş, Turkey, Using GIS-Based Density Analysis

Özcan, Mehmet Musa; Küçükönder, Muhterem

doi:10.1007/s12524-020-01137-0

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…On the one hand, the distribution of traffic accidents can be visualized through GIS visualization technology [7][8][9]. On the other hand, by using a variety of spatial analysis tools in GIS, scholars can explore the spatial distribution characteristics of traffic accidents and the spatial relationship between different traffic accidents from a variety of perspectives [10][11][12]. e most common spatial statistical methods in GIS are density analysis, which accomplishes spatial visualization of accidents through kernel density and point density methods [13][14][15][16], cluster analysis, which can identify the spatial distribution of traffic accidents as aggregation, diffusion, or random distributions by nearest neighbor distance, and Ripley's K function method [17][18][19], which can identify traffic accident hotspot areas by hotspot analysis [20][21][22] and spatial autocorrelation analysis [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

Wang

Chen

et al. 2021

Journal of Advanced Transportation

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Road traffic safety is a social issue of widespread concern. It is important for traffic managers to understand the distribution patterns of road traffic accidents. To this end, this study examines the spatial and temporal patterns of road traffic accidents from both accident frequency and accident severity perspectives. Road traffic accident data from 2016 to 2018 in Harbin, China, were used for the analysis. First, the spatial localization of accidents was completed using geocoding, and the localized accident data were classified by season. Then, density analysis was performed both with and without considering road network density. The results of the density analysis showed that when road network density was considered, accidents were mainly distributed in urban centers, while accidents were more dispersed when road network density was not considered. Third, a cluster analysis considering accident severity found that low-severity accident clusters occurred mostly in urban centers. High-severity accident clusters were mostly present in suburban areas. Finally, the results of these two methods are shown by using the comap technique. Areas of the city with a high frequency and severity of crashes in each season were identified. This study will help traffic management to have a more visual and intuitive understanding of the urban traffic safety situation and to take targeted measures to improve it accordingly.

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Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

Wang

Chen

et al. 2021

Journal of Advanced Transportation

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“…The output of the KDE method was presented in a raster format. Several researchers declare that the selection of the bandwidth r is more important than the selection of the kernel function k [50][51][52][53]. Technically, three kernel functions can be used to conduct the KDE, namely the Gaussian function, the Quartic function and the Minimum variance function b [54].…”

Section: Kernel Density Estimation Techniquementioning

confidence: 99%

“…The Moran's I Index, is one the most efficient spatial autocorrelation assessments in the GIS environment [52,55]. Basically, it is used to investigate spatial location in order to determine whether nearby areas have similar or dissimilar values [52,56]. Moran's I value ranges from 1 to −1.…”

Section: Global Moran's Indexmentioning

confidence: 99%

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A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

et al. 2022

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The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify risk of traffic accident hotspots. Accident data for the time period of April 2018 to November 2020 were obtained from the traffic police of Tabriz (Iran) and analyzed using GIS spatial and network analysis procedures. To evaluate the impacts of COVID-19, we used the seasonal variation in car accidents to analyze the change in the total number or urban traffic accidents. Eventually, the sustainability of urban transport was analyzed based on the demographic and land use data to identify the areas with a high number of accidents and its respective impacts for the local residences. Based on the results, the lockdown measures in response to the pandemic have led to significant reductions in road traffic accidents. From the perspective of urban planning, the spatiotemporal urban traffic accident analysis indicated that areas with high numbers of elderly people and children were most affected by car accidents. As we identified the hotspots of urban traffic accidents and evaluated their spatiotemporal correlation with land use and demography characteristics, we conclude that the results of this study can be used by urban managers and support decision making to improve the situation, so that fewer accidents will happen in the future.

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“…Moda transportasi yang mendominasi insiden kecelakaan lalu lintas dan menimbulkan dampak serius bagi korban adalah sepeda motor meskipun moda ini dinilai praktis (Meyyappan et al, 2018). Penelitian lainnya menunjukkan sebagian besar kecelakaan lalu lintas dapat terjadi di persimpangan jalan setiap hari selama jamjam sibuk (pukul 07.00-10.00 dan pukul 17.00-21.00) (Özcan & Küçükönder 2020). Studi tentang faktor-faktor ini akan membantu untuk mengetahui penyebab faktual kecelakaan lalu lintas dan menetapkan prioritas untuk pencegahan keparahan cedera akibat kecelakaan.…”

Section: Pendahuluanunclassified

Epidemiologi Kecelakaan Lalu Lintas: Tantangan Dan Solusi

Zainafree¹,

Syukria²,

Addina³

et al. 2022

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Kecelakaan lalu lintas menjadi salah satu penyebab kematian ke-8 di Indonesia dan merupakan penyebab utama kematian pada usia 14– 40 tahun. Secara global, kecelakaan lalu lintas juga menjadi penyebab pertama kematian akibat cedera dengan jumlah terbanyak. Terdapat kenaikan angka kejadian kecelakaan lalu lintas pertahunnya di Indonesia, begitu pula kenaikan jumlah korban meninggal dan luka-luka. Kota Semarang merupakan kota dengan kejadian kecelakaan yang cukup tinggi. Angka kejadian kecelakaan di Kota Semarang meningkat dan jumlah korban terus bertambah, maka diperlukan suatu tindakan untuk mencegah terjadinya hal tersebut. Kajian epidemiologi deskriptif dilakukan guna mendapatkan berbagai informasi yang berkaitan dengan kecelakaan. Faktor kelalaian manusia, kendaraan dan faktor lingkungan dikaji berdasarkan data yang tersedia untuk menemukan faktor-faktor berkontribusi terhadap terjadinya kecelakaan lalu lintas. Kajian epidemiologi analitik juga dilakukan guna mendapatkan hasil analisis mendalam terkait data yang telah ditampilkan kemudian dikaji menggunakan berbagai penelitian serupa yang telah dilakukan di daerah lain. Hasil akhir semua kajian akan didapatkan berbagai tantangan dalam mencegah terjadinya kecelakaan lalu lintas. Berbagai solusi juga ditawarkan sebagai jawaban dari tantangan yang tersedia untuk mencegah kejadian kecelakaan lalu lintas dan mencegah jatuhnya korban lebih banyak.

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Investigation of Spatiotemporal Changes in the Incidence of Traffic Accidents in Kahramanmaraş, Turkey, Using GIS-Based Density Analysis

Cited by 10 publications

References 30 publications

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

Spatial and Temporal Distribution Analysis of Traffic Accidents Using GIS-Based Data in Harbin

A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

Epidemiologi Kecelakaan Lalu Lintas: Tantangan Dan Solusi

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