Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

Garefalakis, Thodoris; Katrakazas, Christos; Yannis, George

doi:10.3390/s22145309

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…The proposed hybrid model used three machine learning regularization algorithms, namely: Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator Regression (LR), and Elastic Net Regression (EnetR) for variable selection. The primary knowledge behind these models is the regularization of least squares through a regularization parameter λ [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

Jahanjoo,

Asghari-Jafarabadi,

Sadeghi-Bazargani

2023

BMC Med Res Methodol

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Background Determining risk factors of single-vehicle run-off-road (SV-ROR) crashes, as a significant number of all the single-vehicle crashes and all the fatalities, may provide infrastructure for quicker and more effective safety measures to explore the influencing and moderating variables in SV-ROR. Therefore, this paper emphasizes utilizing a hybrid of regularization method and generalized path analysis for studying SV-ROR crashes to identify variables influencing their happening and severity. Methods This cross-sectional study investigated 724 highway SV-ROR crashes from 2015 to 2016. To drive the key variables influencing SV-ROR crashes Ridge, Least Absolute Shrinkage and Selection Operator (Lasso), and Elastic net regularization methods were implemented. The goodness of fit of utilized methods in a testing sample was assessed using the deviance and deviance ratio. A hybrid of Lasso regression (LR) and generalized path analysis (gPath) was used to detect the cause and mediators of SV-ROR crashes. Results Findings indicated that the final modified model fitted the data accurately with $${\mathcal{X}}_{3}^{2}$$ X 3 2 = 16.09, P < .001, $${\mathcal{X}}^{2}$$ X 2 / degrees of freedom = 5.36 > 5, CFI = .94 > .9, TLI = .71 < .9, RMSEA = 1.00 > .08 (90% CI = (.06 to .15)). Also, the presence of passenger (odds ratio (OR) = 2.31, 95% CI = (1.73 to 3.06)), collision type (OR = 1.21, 95% CI = (1.07 to 1.37)), driver misconduct (OR = 1.54, 95% CI = (1.32 to 1.79)) and vehicle age (OR = 2.08, 95% CI = (1.77 to 2.46)) were significant cause of fatality outcome. The proposed causal model identified collision type and driver misconduct as mediators. Conclusions The proposed HLR-gPath model can be considered a useful theoretical structure to describe how the presence of passenger, collision type, driver misconduct, and vehicle age can both predict and mediate fatality among SV-ROR crashes. While notable progress has been made in implementing road safety measures, it is essential to emphasize that operative preventative measures still remain the most effective approach for reducing the burden of crashes, considering the critical components identified in this study.

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Section: Methodsmentioning

confidence: 99%

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

Jahanjoo,

Asghari-Jafarabadi,

Sadeghi-Bazargani

2023

BMC Med Res Methodol

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“…Similarly, with the help of data mining techniques such as decision tree, Naive Bayes, and artificial neural network (ANN), other kinematic data such as gear position and wheel suspensions from CAN (Controller Area Network) bus can also be utilized to classify driving environments according to [8]. More recently, one noticeable method is proposed in [21], where the objective is to estimate the driving behavior and crash risk from onboard vehicle data such as speed, travel distance, and hand-on-wheel event. To achieve that, a variety of multiclass classifiers are investigated, such as Support Vector Machine (SVM), Random Forest, AdaBoost, and Multilayer Perceptron (MLP).…”

Section: Introductionmentioning

confidence: 99%

Driving Environment Inference from POI of Navigation Map: Fuzzy Logic and Machine Learning Approaches

Li,

Metzner,

Schwieger

2023

Sensors

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To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle’s vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved.

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Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium

Roussou,

Michelaraki,

Katrakazas

et al. 2024

Eur. Transp. Res. Rev.

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The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

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Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

Cited by 3 publications

References 51 publications

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

Driving Environment Inference from POI of Navigation Map: Fuzzy Logic and Machine Learning Approaches

Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium

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