Research graph
References from Uncovering gender-specific motorcycle injury severity mechanisms on thai national highways using hierarchical explainable machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
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Distribution of road traffic deaths by road user group: a global comparison
10.1136/ip.2008.018721 · 2009 · External reference
Factors affecting severity of motorcycle accidents on Thailand's arterial roads: multiple correspondence analysis and ordered logistics regression approaches
10.1016/j.iatssr.2021.10.006 · 2022 · External reference
Evaluating alternate discrete outcome frameworks for modeling crash injury severity
10.1016/j.aap.2013.06.040 · 2013 · External reference
Analytic methods in accident research: methodological frontier and future directions
2014 · External reference
Unobserved heterogeneity and the statistical analysis of highway accident data
2016 · External reference
Comparison of four statistical and machine learning methods for crash severity prediction
10.1016/j.aap.2017.08.008 · 2017 · External reference
Data mining approach to model bus crash severity in Australia
10.1016/j.jsr.2020.12.004 · 2021 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Interpretable machine learning
2020 · External reference
Tree-based approaches to understanding factors influencing crash severity across roadway classes: a Thailand case study
10.1016/j.iatssr.2024.09.001 · 2024 · External reference
Factors affecting single and multivehicle motorcycle crashes: insights from day and night analysis using XGBoost-SHAP algorithm
10.3390/bdcc8100128 · 2024 · External reference
Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: a correlated random parameters ordered probit approach with heterogeneity in means
10.1016/j.aap.2021.106352 · 2021 · External reference
Explaining sex differences in motorcyclist riding behavior: an application of multi-group structural equation modeling
10.3390/ijerph17238797 · 2020 · External reference
Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset
10.1016/j.iatssr.2023.05.003 · 2023 · External reference
Severity prediction of traffic accident using an artificial neural network
10.1002/for.2425 · 2017 · External reference
Factors affecting injury severity at pedestrian crossing locations with rectangular RAPID flashing beacons (RRFB) using XGBoost and random parameters discrete outcome models
10.1016/j.aap.2022.106937 · 2023 · External reference
Exploring e-scooter risk factors based on interpretable machine learning framework
10.1016/j.jsr.2025.06.011 · 2025 · External reference
A random forest and SHAP-based analysis of motorcycle crash severity in Thailand: urban-rural and day-night perspectives
10.1016/j.treng.2025.100369 · 2025 · External reference
Artificial intelligence techniques in road safety for sustainable mobility: an investigation into applications, challenges, and opportunities
10.1016/b978-0-443-33740-6.00009-8 · 2026 · External reference
Enhancing support vector machine performance in forecasting the number of vehicles involved in traffic crashes via metaheuristic optimization algorithms
2025 · External reference
Optuna: a next-generation hyperparameter optimization framework
2019 · External reference
Random search for hyper-parameter optimization
2012 · External reference
A study of cross-validation and bootstrap for accuracy estimation and model selection
1995 · External reference
The class imbalance problem: a systematic study
10.3233/ida-2002-6504 · 2002 · External reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
Detecting hypoglycemia incidents reported in patients’ secure messages: using cost-sensitive learning and oversampling to reduce data imbalance
10.2196/11990 · 2019 · External reference
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
2005 · External reference
A study of the behavior of several methods for balancing machine learning training data
10.1145/1007730.1007735 · 2004 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Greedy function approximation: a gradient boosting machine
2001 · External reference
Xgboost: a scalable tree boosting system
2016 · External reference
Unresolved reference
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CatBoost: unbiased boosting with categorical features
2018 · External reference
"Why should i trust you?" explaining the predictions of any classifier
2016 · External reference
Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways
10.1016/j.aap.2017.08.009 · 2017 · External reference
The role of alcohol in Thailand motorcycle crashes
10.1016/j.aap.2004.07.006 · 2005 · External reference
Impact of COVID-19 on road crashes in Thailand
10.1016/j.iatssr.2024.04.001 · 2024 · External reference
The seven dangerous days: thailand's biannual road traffic accident surges linked to inequality
2025 · External reference
Heat-induced risks of road crashes among older motorcyclists: evidence from three motorcycle-dominant cities in Taiwan
10.1016/j.jth.2023.101754 · 2024 · External reference
Traffic safety. Science serving society
2004 · External reference
Exploring the overall and specific crash severity levels at signalized intersections
10.1016/j.aap.2004.11.002 · 2005 · External reference
Injury severity of motorcycle riders involved in traffic crashes in Hunan, China: a mixed ordered logit approach
10.3390/ijerph13070714 · 2016 · External reference
Modeling of motorcyclist injury severities: a comparison between crashes on main-, frontage-, and standard-lane of roadway
10.1016/j.iatssr.2024.06.005 · 2024 · External reference
Risk factors affecting driver severity of single-vehicle run off road crash for Thailand highway
10.4186/ej.2020.24.5.207 · 2020 · External reference
A random parameters copula-based binary logit-generalized ordered logit model with parameterized dependency: application to active traveler injury severity analysis
2023 · External reference
Severity prediction of traffic accident using an artificial neural network
10.1002/for.2425 · ExternalCitation · doi-reference
Artificial intelligence techniques in road safety for sustainable mobility: an investigation into applications, challenges, and opportunities
10.1016/b978-0-443-33740-6.00009-8 · ExternalCitation · doi-reference
The role of alcohol in Thailand motorcycle crashes
10.1016/j.aap.2004.07.006 · ExternalCitation · doi-reference
Exploring the overall and specific crash severity levels at signalized intersections
10.1016/j.aap.2004.11.002 · ExternalCitation · doi-reference
Evaluating alternate discrete outcome frameworks for modeling crash injury severity
10.1016/j.aap.2013.06.040 · ExternalCitation · doi-reference
Comparison of four statistical and machine learning methods for crash severity prediction
10.1016/j.aap.2017.08.008 · ExternalCitation · doi-reference
Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways
10.1016/j.aap.2017.08.009 · ExternalCitation · doi-reference
Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: a correlated random parameters ordered probit approach with heterogeneity in means
10.1016/j.aap.2021.106352 · ExternalCitation · doi-reference
Factors affecting injury severity at pedestrian crossing locations with rectangular RAPID flashing beacons (RRFB) using XGBoost and random parameters discrete outcome models
10.1016/j.aap.2022.106937 · ExternalCitation · doi-reference
Factors affecting severity of motorcycle accidents on Thailand's arterial roads: multiple correspondence analysis and ordered logistics regression approaches
10.1016/j.iatssr.2021.10.006 · ExternalCitation · doi-reference
Predicting child occupant crash injury severity in the United Arab Emirates using machine learning models for imbalanced dataset
10.1016/j.iatssr.2023.05.003 · ExternalCitation · doi-reference
Impact of COVID-19 on road crashes in Thailand
10.1016/j.iatssr.2024.04.001 · ExternalCitation · doi-reference
Modeling of motorcyclist injury severities: a comparison between crashes on main-, frontage-, and standard-lane of roadway
10.1016/j.iatssr.2024.06.005 · ExternalCitation · doi-reference
Tree-based approaches to understanding factors influencing crash severity across roadway classes: a Thailand case study
10.1016/j.iatssr.2024.09.001 · ExternalCitation · doi-reference
Data mining approach to model bus crash severity in Australia
10.1016/j.jsr.2020.12.004 · ExternalCitation · doi-reference
Exploring e-scooter risk factors based on interpretable machine learning framework
10.1016/j.jsr.2025.06.011 · ExternalCitation · doi-reference
Heat-induced risks of road crashes among older motorcyclists: evidence from three motorcycle-dominant cities in Taiwan
10.1016/j.jth.2023.101754 · ExternalCitation · doi-reference
A random forest and SHAP-based analysis of motorcycle crash severity in Thailand: urban-rural and day-night perspectives
10.1016/j.treng.2025.100369 · ExternalCitation · doi-reference
Random forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
Distribution of road traffic deaths by road user group: a global comparison
10.1136/ip.2008.018721 · ExternalCitation · doi-reference
A study of the behavior of several methods for balancing machine learning training data
10.1145/1007730.1007735 · ExternalCitation · doi-reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · ExternalCitation · doi-reference
Detecting hypoglycemia incidents reported in patients’ secure messages: using cost-sensitive learning and oversampling to reduce data imbalance
10.2196/11990 · ExternalCitation · doi-reference
The class imbalance problem: a systematic study
10.3233/ida-2002-6504 · ExternalCitation · doi-reference
Factors affecting single and multivehicle motorcycle crashes: insights from day and night analysis using XGBoost-SHAP algorithm
10.3390/bdcc8100128 · ExternalCitation · doi-reference
Injury severity of motorcycle riders involved in traffic crashes in Hunan, China: a mixed ordered logit approach
10.3390/ijerph13070714 · ExternalCitation · doi-reference
Explaining sex differences in motorcyclist riding behavior: an application of multi-group structural equation modeling
10.3390/ijerph17238797 · ExternalCitation · doi-reference
Risk factors affecting driver severity of single-vehicle run off road crash for Thailand highway
10.4186/ej.2020.24.5.207 · ExternalCitation · doi-reference
