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Implementing recommendations from accident investigations: A case study of inter-organisational challenges
Implementing recommendations from accident investigations: A case study of inter-organisational challenges
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Implementing recommendations from accident investigations: A case study of inter-organisational challenges

Cedergren, Alexander

Accident analysis and prevention, 2013-04, Vol.53, p.133-141 [Periódico revisado por pares]

Kidlington: Elsevier Ltd

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2
Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP
Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP
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Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP

Wen, Xiao ; Xie, Yuanchang ; Wu, Lingtao ; Jiang, Liming

Accident analysis and prevention, 2021-09, Vol.159, p.106261-106261, Article 106261 [Periódico revisado por pares]

Elsevier Ltd

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3
A feature learning approach based on XGBoost for driving assessment and risk prediction
A feature learning approach based on XGBoost for driving assessment and risk prediction
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A feature learning approach based on XGBoost for driving assessment and risk prediction

Shi, Xiupeng ; Wong, Yiik Diew ; Li, Michael Zhi-Feng ; Palanisamy, Chandrasekar ; Chai, Chen

Accident analysis and prevention, 2019-08, Vol.129, p.170-179 [Periódico revisado por pares]

England: Elsevier Ltd

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4
Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis
Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis
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Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

Parsa, Amir Bahador ; Movahedi, Ali ; Taghipour, Homa ; Derrible, Sybil ; Mohammadian, Abolfazl (Kouros)

Accident analysis and prevention, 2020-03, Vol.136, p.105405-105405, Article 105405 [Periódico revisado por pares]

England: Elsevier Ltd

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5
Crash data augmentation using variational autoencoder
Crash data augmentation using variational autoencoder
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Crash data augmentation using variational autoencoder

Islam, Zubayer ; Abdel-Aty, Mohamed ; Cai, Qing ; Yuan, Jinghui

Accident analysis and prevention, 2021-03, Vol.151, p.105950-105950, Article 105950 [Periódico revisado por pares]

England: Elsevier Ltd

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6
A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling
A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling
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A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling

Wang, Chen ; Xie, Yuanchang ; Huang, Helai ; Liu, Pan

Accident analysis and prevention, 2021-07, Vol.157, p.106157-106157, Article 106157 [Periódico revisado por pares]

England: Elsevier Ltd

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7
Real-time crash risk prediction on arterials based on LSTM-CNN
Real-time crash risk prediction on arterials based on LSTM-CNN
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Real-time crash risk prediction on arterials based on LSTM-CNN

Li, Pei ; Abdel-Aty, Mohamed ; Yuan, Jinghui

Accident analysis and prevention, 2020-02, Vol.135, p.105371-105371, Article 105371 [Periódico revisado por pares]

England: Elsevier Ltd

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8
What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records
What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records
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What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records

Rolison, Jonathan J. ; Regev, Shirley ; Moutari, Salissou ; Feeney, Aidan

Accident analysis and prevention, 2018-06, Vol.115, p.11-24 [Periódico revisado por pares]

England: Elsevier Ltd

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9
Comparison of four statistical and machine learning methods for crash severity prediction
Comparison of four statistical and machine learning methods for crash severity prediction
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Comparison of four statistical and machine learning methods for crash severity prediction

Iranitalab, Amirfarrokh ; Khattak, Aemal

Accident analysis and prevention, 2017-11, Vol.108, p.27-36 [Periódico revisado por pares]

England: Elsevier Ltd

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10
Half-century research developments in maritime accidents: Future directions
Half-century research developments in maritime accidents: Future directions
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Half-century research developments in maritime accidents: Future directions

LUO, MEIFENG ; SHIN, SUNG-HO

Accident analysis and prevention, 2019-02, Vol.123, p.448-460 [Periódico revisado por pares]

England: Elsevier Ltd

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