An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Çodur, Muhammed Yasin; Tortum, Ahmet

doi:10.7307/ptt.v27i3.1551

Cited by 66 publications

(37 citation statements)

References 20 publications

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“…ANNs was also applied to determine the relationship between crash severity and the model parameters including years, highway sections, section length (km), AADT, the degree of horizontal curvature, the degree of vertical curvature, heavy vehicles (percentage), and season summer (percentage). The results shown that degree of vertical curvature has strong impact on number of crashes [28]. By modeling AADT, SL (Posted speed limit), Gradient (Average segment gradient), and Curvature (Average segment curvature) against road crashes, it was concluded that ANN was superior to multivariate Poisson-lognormal models [29].…”

Section: Anns For Road Safety Analysismentioning

confidence: 97%

Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

et al. 2017

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Identification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis.

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Section: Anns For Road Safety Analysismentioning

confidence: 97%

Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

et al. 2017

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“…Much of the recent work in this field focuses on enhancement techniques and methods of increasing the signal-to-noise ratio [14][15][16][17]. Methods like linear regression, logic regression model, Poisson model and negative binominal model are subject to strong assumption and limitations in applications [18]. Some methods are more commonly used; however, this does not state its accuracy in prediction.…”

Section: Literature Overviewmentioning

confidence: 99%

Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

Mrgole

Sever

2017

PROMET

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“…Çodur, yapay sinir ağları yöntemini kullanarak Erzurum ili karayolu trafik kazalarının analizini yapmıştır. Yapay sinir ağları ile yapılan analizlerde karayolu trafik kazalarında en etkili parametrenin düşey kurblar olduğu tespit edilmiştir [34]. Akgüngör ve Doğan; regresyon analizi, yapay sinir ağları ve genetik algoritma yöntemlerini kullanarak İzmir ili için trafik kaza tahmin modelleri geliştirmişlerdir.…”

Section: Ftunclassified

A new approach for road traffic accidents: Crash analysis segments model

Güler¹

2017

Pamukkale J Eng Sci

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Bu çalışmada kaza analizleri konusunda yapılan araştırmaların geniş bir literatür taraması yapılarak kaza analiz kesimleri adında yeni bir yaklaşım modeli geliştirilmiştir. Bu yaklaşımda, kaza analizi yapılacak olan yolun geometrik ve trafik özellikleri dikkate alınarak yol kesimi homojen özellikte kaza analiz kesimlerine bölünmüştür. Çalışma bölgesinde meydana gelen trafik kazalarına etki eden faktörler ve bu faktörlerin sebep olduğu kaza şiddetleri belirlenmiştir. Her bir analiz kesimine ait geçmiş trafik kazaları belirli bir zaman periyodu için incelenmiş, kaza şiddetlerine ve kazaya etki eden faktörlere ağırlık değerleri verilmiştir. Gerçekleştirilen analizlerle her bir kaza analiz kesimi, kaza açısından önem derecesine göre sıralanmıştır. Sonuç olarak, geliştirilen model verileri teorik olarak üretilen kuramsal bir yol kesiminde test edilmiş, diğer kaza analiz modelleriyle karşılaştırılmış ve güvenilir sonuçlar elde edilmiştir. In this study a comprehensive literature review was done on road traffic accident studies and a new approach named crash analysis

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An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Abstract: ABSTRACT

Cited by 66 publications

References 20 publications

Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

A new approach for road traffic accidents: Crash analysis segments model

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