Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

Gupta, Rajesh; Asgari, Hamidreza; Azimi, Ghazaleh; Rahimi, Alireza; Jin, Xia

doi:10.1177/03611981211033278

Cited by 8 publications

(6 citation statements)

References 89 publications

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“…The choice of adopting SMOTE, consistent with Orsini et al (3), was mainly because of the limited sample size available, which would have made RUMC unfeasible, in particular for short data collection durations. It is also worth noting that the practice of using SMOTE is becoming quite established in road safety modeling, and many recent works have applied it with good results (22)(23)(24)(25)(26)(27).…”

Section: Methodsmentioning

confidence: 99%

Conflict-Based Real-Time Road Safety Analysis: Sensitivity to Data Collection Duration and its Implications for Model Resilience

Orsini

Gastaldi

Rossi

2023

Transportation Research Record: Journal of the Transportation R

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Conflict-based approaches to real-time road safety analysis can provide several benefits over traditional crash-based models. In particular, as traffic conflicts are much more frequent than crashes, models can be trained with significantly shorter collection periods. Since existing literature has not investigated the sensitivity of real-time conflict prediction models (RTConfPM) to data collection duration, here we aim to fill this gap and discuss the implications for model resilience. A real-world highway case study was analyzed. Methodologically, various traffic variables aggregated into 5 min intervals were selected as predictors, synthetic minority oversampling technique (SMOTE) was applied to deal with unbalanced classification issues, and support vector machine (SVM) was chosen as classifier. The dichotomous response variable separated safe and unsafe intervals into two classes; the latter were defined considering a minimum number of rear-end conflicts within the interval, which were identified using a surrogate measure of safety (SMS), that is, time-to-collision. Several RTConfPMs were trained and tested, considering different data collection durations and different criteria to define the unsafe situation class. The results, which were shown to be robust with respect to the machine learning classifier used, indicate that the models were able to provide reliable predictions with just three to five days of data, and that the increase in performance with collection periods longer than 10 to 15 days was negligible. These findings can be generalized by considering the number of unsafe situations corresponding to the data collection period of each tested model; they highlight the relevance of RTConfPM as a more flexible and resilient alternative to the crash-based approach.

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Section: Methodsmentioning

confidence: 99%

Conflict-Based Real-Time Road Safety Analysis: Sensitivity to Data Collection Duration and its Implications for Model Resilience

Orsini

Gastaldi

Rossi

2023

Transportation Research Record: Journal of the Transportation R

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“…However, using traditional resampling techniques like RUMC can result in the loss of crucial information [23,32]. Consequently, it is vital to explore synthetic resampling strategies as an alternative approach to effectively tackle this issue [33][34][35][36]. Despite the potential benefits of synthetic resampling strategies, comprehensive comparative analyses assessing the predictive power of parametric and non-parametric machine learning techniques in conjunction with such strategies remain scarce.…”

Section: Applications Of Machine Learning Models In Crash Severity Pr...mentioning

confidence: 99%

Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques

2023

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As the global elderly population continues to rise, the risk of severe crashes among elderly drivers has become a pressing concern. This study presents a comprehensive examination of crash severity among this demographic, employing machine learning models and data gathered from Virginia, United States of America, between 2014 and 2021. The analysis integrates parametric models, namely logistic regression and linear discriminant analysis (LDA), as well as non-parametric models like random forest (RF) and extreme gradient boosting (XGBoost). Central to this study is the application of resampling techniques, specifically, random over-sampling examples (ROSE) and the synthetic minority over-sampling technique (SMOTE), to address the dataset’s inherent imbalance and enhance the models’ predictive performance. Our findings reveal that the inclusion of these resampling techniques significantly improves the predictive power of parametric models, notably increasing the true positive rate for severe crash prediction from 6% to 60% and boosting the geometric mean from 25% to 69% in logistic regression. Likewise, employing SMOTE resulted in a notable improvement in the non-parametric models’ performance, leading to a true positive rate increase from 8% to 36% in XGBoost. Moreover, the study established the superiority of parametric models over non-parametric counterparts when balanced resampling techniques are utilized. Beyond predictive modeling, the study delves into the effects of various contributing factors on crash severity, enhancing the understanding of how these factors influence elderly road safety. Ultimately, these findings underscore the immense potential of machine learning models in analyzing complex crash data, pinpointing factors that heighten crash severity, and informing targeted interventions to mitigate the risks of elderly driving.

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“…Regarding the influence of weather and lighting conditions on crash severity, the results in the literature are inconsistent. It should be noted that while some studies ranked the weather and lighting conditions as one of the main predictors of the crash severity outcome [19,31,75], others are in accordance with the findings of this paper, indicating the insignificance of these variables on crash severity [23,35]. In addition, the significance of the temporal factors has been confirmed in the literature of subject as Zheng et al [19] identified the importance of time of day and weekday in the severity levels of crashes involving trucks.…”

Section: Plos Onementioning

confidence: 99%

Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data

2023

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While the cost of road traffic fatalities in the U.S. surpasses $240 billion a year, the availability of high-resolution datasets allows meticulous investigation of the contributing factors to crash severity. In this paper, the dataset for Trucks Involved in Fatal Accidents in 2010 (TIFA 2010) is utilized to classify the truck-involved crash severity where there exist different issues including missing values, imbalanced classes, and high dimensionality. First, a decision tree-based algorithm, the Synthetic Minority Oversampling Technique (SMOTE), and the Random Forest (RF) feature importance approach are employed for missing value imputation, minority class oversampling, and dimensionality reduction, respectively. Afterward, a variety of classification algorithms, including RF, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Gradient-Boosted Decision Trees (GBDT), and Support Vector Machine (SVM) are developed to reveal the influence of the introduced data preprocessing framework on the output quality of ML classifiers. The results show that the GBDT model outperforms all the other competing algorithms for the non-preprocessed crash data based on the G-mean performance measure, but the RF makes the most accurate prediction for the treated dataset. This finding indicates that after the feature selection is conducted to alleviate the computational cost of the machine learning algorithms, bagging (bootstrap aggregating) of decision trees in RF leads to a better model rather than boosting them via GBDT. Besides, the adopted feature importance approach decreases the overall accuracy by only up to 5% in most of the estimated models. Moreover, the worst class recall value of the RF algorithm without prior oversampling is only 34.4% compared to the corresponding value of 90.3% in the up-sampled model which validates the proposed multi-step preprocessing scheme. This study also identifies the temporal and spatial (roadway) attributes, as well as crash characteristics, and Emergency Medical Service (EMS) as the most critical factors in truck crash severity.

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Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

Cited by 8 publications

References 89 publications

Conflict-Based Real-Time Road Safety Analysis: Sensitivity to Data Collection Duration and its Implications for Model Resilience

Conflict-Based Real-Time Road Safety Analysis: Sensitivity to Data Collection Duration and its Implications for Model Resilience

Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques

Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data

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