Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Kaygisiz, Ömür; Düzgün, Şebnem; Yildiz, Ahmet; Senbil, Metin

doi:10.1016/j.trf.2015.07.002

Cited by 32 publications

(11 citation statements)

References 22 publications

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“…This threshold value was used to identify hotspots.Application of NKDE on traffic analysis has been well explored by researchers all over the world. Kaygisiz et. al.…”

Section: Fig3 Difference Between Pkde and Nkde In Estimating Densitmentioning

confidence: 99%

“…al. (2015) performed a spatio-temporal analysis to determine the impact of behavior factors in driving causing traffic accidents using Network KDEand suggested changes in road structure pertaining to behavior aspects in driving [47]. The authors tested the statistical significance of the density estimate with nearest neighbor and K function analysis and temporal changes in hotspot was tested with chi square test.…”

Section: Fig3 Difference Between Pkde and Nkde In Estimating Densitmentioning

confidence: 99%

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Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

Srikanth¹,

Srikanth²,

Arockiasamy³

2019

IJEAT

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Limiting the number andseverity of traffic accidents is one of the major goals of road traffic safety management.The alarming rate of road accidents globally emphasizes the importance of an effective traffic safety management system. Identification of accident hotspots is the first step towards implementation of efficient traffic safety management.Until the arrival of Geographical Information System (GIS),traffic accident analyses have been performed based ontraditional statistical methods alone. The advent of GIS-based techniques has led toimproved traffic accident analysis by employing spatial statistics,enabling engineers and researchers to account for variation in the spatial characteristics of hotspot locations in the analysis. This paper discusses the different spatial and statistical methods that are employedintraffic accident hotspots identification. An example application of Planar Kernel Density Estimation (PKDE)for hotspot identification is presented based on crash data for Des Moines city of Iowa state. The effect of varying bandwidths in creating density mapsis investigated and the optimum bandwidth to obtain distinct hotspots is identified as 500 m for the chosen study area.The paper also discusses the scope for future research in traffic accident hotspot analysis.

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“…This threshold value was used to identify hotspots.Application of NKDE on traffic analysis has been well explored by researchers all over the world. Kaygisiz et. al.…”

Section: Fig3 Difference Between Pkde and Nkde In Estimating Densitmentioning

confidence: 99%

Section: Fig3 Difference Between Pkde and Nkde In Estimating Densitmentioning

confidence: 99%

Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

Srikanth¹,

Srikanth²,

Arockiasamy³

2019

IJEAT

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show abstract

“…), karayolu geometrik özellikleri (yol sınıfı, yüzey koşulları, yatay ve düşey dönemeçlerin varlığı, bölünmüş/bölünmemiş yol vb.) gibi kaza düzeyinde çok detaylı veriye ihtiyaç duymaktadır [11,17] [19][20][21][22][23][24]. Modelleme aşamasında kaza düzeyinde yeteri kadar detaylı veri kullanan iki yerel çalışma dikkat çekmektedir.…”

Section: Li̇teratür öZeti̇unclassified

Türkiye’de Devlet Karayollarında Kaza Oranlarının ve Kaza Örüntüsünün Analizi

Özen

Zorlu

2018

Teknik Dergi

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Bu çalışmada Türkiye'de 2009 ile 2013 yılları arasında devlet karayolları kesimlerinde istatistiksel ve coğrafi yöntemler kullanılarak kaza analizleri yapılmıştır. Çalışmanın coğrafi analizler aşamasında global Moran's I değeri hesaplanarak kaza oranlarının mekânsal korelasyonunun olup olmadığına bakılmıştır. Kazaların hangi yol kesimlerinde daha yüksek oranda gerçekleştiğini tespit etmek için milyar taşıt-km başına düşen ölümlü ve/veya yaralanmalı (ÖY) kaza sayısı göstergesi kullanılmıştır. Bu kesimlerin coğrafi dağılımı incelendiğinde dağınık (kümelenmemiş) bir örüntü ortaya çıkmaktadır. Ulaşım ve trafik araştırmaları yazınında taşıt hacmi kaza sayısını açıklayan en önemli etkeni olarak gösterilmektedir. Bu çalışmada kaza oranları incelenerek taşıt yoğunluğu dışında pek çok etkenin açıklayıcı olabileceği iddia edilmiştir.

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“…Using a spatial methodology by the K-function has already been well received in the studies on traffic accidents (Yamada and Thill 2004;Xie and Yan 2013), where cluster patterns were detected. Kaygisiz et al (2015) mapped the focus of accidents using network-based kernel density estimation, K-function, and the nearest neighbor distance methods. Furthermore, regarding travel analysis, Dugundji and Walker (2005) explored the interactions between a decision-maker and the aggregate actions of other decision-makers proximally situated in a spatial network by discrete choice analysis using the Biogeme toolkit.…”

Section: Introductionmentioning

confidence: 99%

A Conjoint Approach of Spatial Statistics and a Traditional Method for Travel Mode Choice Issues

Lindner

Pitombo

2017

J geovis spat anal

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Conventional analysis of transportation demand is usually carried out using socioeconomic, travel, and land use attributes. Despite the effectiveness on travel demand forecasting, it is important to recognize that alternative approaches have been developed in recent years. Traditional methods, besides considering different explanatory variables, are appropriate to make estimates exclusively on previously surveyed households. On the other hand, recent studies have addressed spatial statistical concerns in the field of travel demand forecasting. The aim of this paper is to spatially estimate motorized travel mode choice probabilities in a continuous map using an Origin-Destination Survey database, conducted in the São Paulo Metropolitan Area in Brazil in 2007. Values were estimated in both sampled and non-sampled coordinates. This paper proposes a conjoint approach that combines the traditional procedure of travel demand forecasting (multiple logistic regression) with a spatial statistical method (ordinary kriging). A comparison is made with the one-step spatial method-indicator kriging (IK). Conjoint studies of spatial statistics and traditional methods are thriving in transportation analysis, giving rise to a travel mode choice surface in a confirmatory way. It is concluded that the proposed method can be used for future predictions of travel mode choices, unlike IK.

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Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Cited by 32 publications

References 22 publications

Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

Identification of Traffic Accident Hotspots using Geographical Information System (GIS)

Türkiye’de Devlet Karayollarında Kaza Oranlarının ve Kaza Örüntüsünün Analizi

A Conjoint Approach of Spatial Statistics and a Traditional Method for Travel Mode Choice Issues

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