Design and experiment verification of a novel analysis framework for recognition of driver injury patterns: From a multi-class classification perspective

Zhu, Mengtao; Li, Yunjie; Wang, Yinhai

doi:10.1016/j.aap.2018.08.011

Cited by 26 publications

(11 citation statements)

References 46 publications

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“…Likewise, our results agree with the studies that applied a non-parametric model analysis, where the concurrent variables that increase severity are alcohol and drug influence (psychophysical conditions) and the non-use of a safety belt, as shown in the references [26,28,29], especially in RO collision types [35] and in the case of an ROR collision [36]. Moreover, no factor alone is a key determinant; only a combination of factors are [26].…”

Section: Discussionsupporting

confidence: 90%

“…Among the non-traditional methods with two or multiple answer variables, there are the Artificial Neuronal Network (ANN) [26] and Classification And Regression Trees (CARTs) [27,28]. Similarly, other advanced tools have been combined, such as Machine Learning (ML) methods, including Conditional Inference Trees and Forest [29], Decision Trees (DT) and Decision rules (DR) [30][31][32], Random Forest (RF) and Boosted Regression Trees (BRTs) [33], RF and OPM models [34], CART (as a variable selection model) and Support Vector Machine (SVM) (as a predictive model) [35], RF for variable selection and ANN for prediction [36], and comparison ML methods and performance studies [37,38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zhu et al [36] investigated driver injury patterns (four levels: fatal/serious injury, evident injury, possible injury, and no injury) in ROR crashes with multi-class classification, based on an ML analysis (RF and binary ANN models), with records collected in Washington State from 2011 to 2013. Among the exploratory variables, the following were included: time variables, environment, vehicles (passenger car, pickup, truck, and others), and demographic variables.…”

Section: Literature Reviewmentioning

confidence: 99%

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Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

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The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

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Section: Discussionsupporting

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

et al. 2020

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“…Extreme gradient boosting (XGBoost) and random forest were used to select the most effective independent variables. This is in line with recent related studies that deal with a high number of independent variables [27][28][29][30][31]. The random forest aggregates many binary decision trees.…”

Section: Methodssupporting

confidence: 86%

Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian

2022

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The current literature on public perceptions of autonomous vehicles focuses on potential users and the target market. However, autonomous vehicles need to operate in a mixed traffic condition, and it is essential to consider the perceptions of road users, especially vulnerable road users. This paper builds explicitly on the limitations of previous studies that did not include a wide range of road users, especially vulnerable road users who often receive less priority. Therefore, this paper considers the perceptions of vulnerable road users towards sharing roads with autonomous vehicles. The data were collected from 795 people. Extreme gradient boosting (XGBoost) and random forests are used to select the most influential independent variables. Then, a decision tree-based model is used to explore the effects of the selected most effective variables on the respondents who approve the use of public streets as a proving ground for autonomous vehicles. The results show that the effect of autonomous vehicles on traffic injuries and fatalities, being safe to share the road with autonomous vehicles, the Elaine Herzberg accident and its outcome, and maximum speed when operating in autonomous are the most influential variables. The results can be used by authorities, companies, policymakers, planners, and other stakeholders.

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“…Then, the identified effective variables are used as selected variables to explore the effects of these selected variables on the predicted variable. Random forest is a common method for selecting the most effective variables in studies with a high number of predictors ( (Jahangiri, Rakha and Dingus, 2016); (Kitali, Alluri, Sando, Haule, Kidando and Lentz, 2018); (Zhu, Li and Wang, 2018); (Aghaabbasi, Shekari, Shah, Olakunle, Armaghani and Moeinaddini, 2020); (Lu and Ma, 2020)). The random forest aggregates many binary decision trees.…”

Section: Crash Typementioning

confidence: 99%

Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes

Moeinaddini¹,

Pourmoradnasseri²,

Hadachi³

et al. 2023

Preprint

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Different factors have effects on traffic crashes and crash-related injuries. These factors include segment characteristics, crash-level characteristics, occupant level characteristics, environment characteristics, and vehicle level characteristics. There are several studies regarding these factors' effects on crash injuries. However, limited studies have examined the effects of pre-crash events on injuries, especially for curve-related crashes. The majority of previous studies for curve-related crashes focused on the impact of geometric features or street design factors. The current study tries to eliminate the aforementioned shortcomings by considering important precrash events related factors as selected variables and the number of vehicles with or without injury as the predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. The relationships are explored using different machine learning algorithms like the random forest, C5.0, CHAID, Bayesian Network, Neural Network, C&R Tree, Quest, etc. The random forest and SHAP values are used to identify the most effective variables. The C5.0 algorithm, which has the highest accuracy rate among the other algorithms, is used to develop the final model. Analysis results revealed that the extent of the damage, critical pre-crash event, preimpact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or without injury in curve-related crashes.

show abstract

Design and experiment verification of a novel analysis framework for recognition of driver injury patterns: From a multi-class classification perspective

Cited by 26 publications

References 46 publications

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian

Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes

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