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

Accident analysis and prevention, 2020-03, Vol.136, p.105405-105405, Article 105405

2020

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

Accident analysis and prevention, 2021-09, Vol.159, p.106261-106261, Article 106261

2021

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

Accident analysis and prevention, 2019-08, Vol.129, p.170-179

2019

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

Accident analysis and prevention, 2021-07, Vol.157, p.106157-106157, Article 106157

2021

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Crash data augmentation using variational autoencoder

Accident analysis and prevention, 2021-03, Vol.151, p.105950-105950, Article 105950

2021

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

Accident analysis and prevention, 2020-02, Vol.135, p.105371-105371, Article 105371

2020

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Understanding take-over performance of high crash risk drivers during conditionally automated driving

Accident analysis and prevention, 2020-08, Vol.143, p.105543-105543, Article 105543

2020

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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

Accident analysis and prevention, 2018-06, Vol.115, p.11-24

2018

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

Accident analysis and prevention, 2017-11, Vol.108, p.27-36

2017

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Evaluating the safety impact of connected and autonomous vehicles on motorways

Accident analysis and prevention, 2019-03, Vol.124, p.12-22

2019

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Visualization and analysis of mapping knowledge domain of road safety studies

Accident analysis and prevention, 2018-09, Vol.118, p.131-145

2018

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The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis

Accident analysis and prevention, 2021-08, Vol.158, p.106153-106153, Article 106153

2021

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Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances

Accident analysis and prevention, 2019-02, Vol.123, p.12-19

2019

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Longitudinal safety evaluation of connected vehicles’ platooning on expressways

Accident analysis and prevention, 2018-08, Vol.117, p.381-391

2018

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Safety of micro-mobility: Analysis of E-Scooter crashes by mining news reports

Accident analysis and prevention, 2020-08, Vol.143, p.105608-105608, Article 105608

2020

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A statistical study of railway safety in China and Japan 1990–2020

Accident analysis and prevention, 2022-09, Vol.175, p.106764-106764, Article 106764

2022

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A systematic mapping review of surrogate safety assessment using traffic conflict techniques

Accident analysis and prevention, 2021-04, Vol.153, p.106016-106016, Article 106016

2021

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A context-aware driver model for determining recommended speed in blind intersection situations

Accident analysis and prevention, 2021-12, Vol.163, p.106447-106447, Article 106447

2021

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An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors

Accident analysis and prevention, 2021-09, Vol.160, p.106322-106322, Article 106322

2021

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A review of spatial approaches in road safety

Accident analysis and prevention, 2020-02, Vol.135, p.105323-105323, Article 105323

2020

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Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas

Accident analysis and prevention, 2017-09, Vol.106, p.358-369

2017

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Psychosocial factors associated with intended use of automated vehicles: A simulated driving study

Accident analysis and prevention, 2018-06, Vol.115, p.202-208

2018

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A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data

Accident analysis and prevention, 2019-01, Vol.122, p.239-254

2019

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Real-time accident detection: Coping with imbalanced data

Accident analysis and prevention, 2019-08, Vol.129, p.202-210

2019

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Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Accident analysis and prevention, 2016-10, Vol.95 (Pt B), p.495-502

2016

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1 – 25 von 7.409

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

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

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

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

Crash data augmentation using variational autoencoder

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

Understanding take-over performance of high crash risk drivers during conditionally automated driving

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

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

Evaluating the safety impact of connected and autonomous vehicles on motorways

Visualization and analysis of mapping knowledge domain of road safety studies

The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis

Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances

Longitudinal safety evaluation of connected vehicles’ platooning on expressways

Safety of micro-mobility: Analysis of E-Scooter crashes by mining news reports

A statistical study of railway safety in China and Japan 1990–2020

A systematic mapping review of surrogate safety assessment using traffic conflict techniques

A context-aware driver model for determining recommended speed in blind intersection situations

An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors

A review of spatial approaches in road safety

Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas

Psychosocial factors associated with intended use of automated vehicles: A simulated driving study

A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data

Real-time accident detection: Coping with imbalanced data

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

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