Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Wei, Tianzheng; Zhu, Tong; Lin, Miao; Liu, Haoxue

doi:10.1080/15389588.2023.2284111

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…In this paper, the evaluation metrics commonly used in machine learning were used, including accuracy, precision, recall, f1-score, and area under the curve (AUC). Typically, a model's performance is considered as excellent if each metric exceeds the 80% threshold [31]. The model constructed from the HAA data showed an accuracy of 95.8%, precision of 96.1%, recall of 97.3%, f1-score of 96.7%, and AUC of 95.3%.…”

Section: Resultsmentioning

confidence: 99%

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Yan,

Hu,

et al. 2024

Sustainability

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Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

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Section: Resultsmentioning

confidence: 99%

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Yan,

Hu,

et al. 2024

Sustainability

View full text Add to dashboard Cite

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Deep Q-learning Network-based Imbalanced Classification for Fatality Prediction of Single-Vehicle Motorcycle Crashes

Zulherman,

Yang,

Shimizu

et al. 2024

Int. J. ITS Res.

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Even though the number of motorcycle crash accidents in Japan has trended downward over the past decade, there persists a necessity to propose effective safety measures targeting factors associated with fatal crash accidents. Previous studies have demonstrated the utility of various machine-learning models in classifying and investigating crash accidents involving motorcycles. However, the comprehensive examination of fatal cases in single-vehicle motorcycle crashes remains limited due to imbalanced crash data between fatal and non-fatal cases. Despite the ability of several conventional machine learning (ML) models to predict motorcycle crash severity, the imbalanced single-vehicle crash data poses a challenge in accurately classifying fatal crashes using ML models. To address this challenge, the deep q-learning network-based imbalanced classification (DQNIC), a modification of Lin’s DQNimb, was employed to classify single-vehicle motorcycle crashes (fatal and non-fatal). Moreover, SHAP value analysis was utilized to interpret the DQNIC model and explore the factors associated with fatal crashes. The 4-year crashes in Japan nationwide are used for empirical study. The significant findings indicate 1) that the DQNIC model demonstrates effective classification of fatal crashes and 2) that factors such as the crash type, road width, terrain, road alignment, and collision point exert significant influence on fatal crashes. Traffic safety professionals can use the findings of this study to implement effective measures to reduce the fatality rate of single-vehicle motorcycle crashes.

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Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning

Zhang,

Jin,

Chang

et al. 2024

International Journal of Transportation Science and Technology

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Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Cited by 5 publications

References 28 publications

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Deep Q-learning Network-based Imbalanced Classification for Fatality Prediction of Single-Vehicle Motorcycle Crashes

Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning

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