A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution

Elassad, Zouhair Elamrani Abou; Mousannif, Hajar; Moatassime, Hassan Al

doi:10.1016/j.knosys.2020.106314

Cited by 44 publications

(22 citation statements)

References 54 publications

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“…These studies report G-mean in the range of 66.39-90.50% which is in accordance with the performances of our models. It is also encouraging to compare the performance of our models with other traffic-related studies [29,30] on imbalanced classification which report Gmean in the range of 70.40-88.50%. It seems that the performance of our models (GRUNN-PC, GRUNN-DV, and GRUNN-T) is on par with these earlier studies.…”

Section: Discussionmentioning

confidence: 59%

“…These elements are used to derive traditional evaluation metrics such as accuracy r= ^st^@ ^st^@t_st_@ u. Having discussed previously that our dataset is an imbalanced dataset with more instances of lane-keeping (majority or negative class) than lane-changing (minority or positive class) ones, traditional evaluation metrics might provide biased results [29,30]. Therefore, we consider geometric mean accuracy or Gmean that integrates recalls of both classes and is used in previous research to classify imbalanced datasets.…”

Section: A Evaluation Metricmentioning

confidence: 99%

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Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

et al. 2022

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This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large trajectory dataset, collected for the Netherlands, consisting of 3,647 trajectories of passenger car drivers, 1,080 trajectories of delivery van drivers, and 2,226 trajectories of truck drivers. We apply gated recurrent unit neural networks to separately model their gap selection processes. These three models can not only handle class imbalance but also capture long-term interdependencies. The models can predict gap selection of three vehicle classes with geometric mean accuracies of 84% or higher.To obtain insights into their gap selection processes, we apply a gradient-based technique to analyze what neural networks have learned. Our results suggest that there exist significant differences between vehicle classes in terms of the importance of historical information and features. Trucks seem to value a relatively longer period, recent 6 seconds, of driving experience to select gaps compared to passenger cars and delivery vans. In addition, the perception of road topology seems to be a significant factor for delivery vans and trucks, contrary to passenger cars which highly value their kinematic features and interactions with surrounding vehicles, to select gaps. These insights offer a novel contribution towards better understanding and modeling of the driving behavior of multiple vehicle classes.

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Section: Discussionmentioning

confidence: 59%

Section: A Evaluation Metricmentioning

confidence: 99%

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

et al. 2022

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“…The highest performances were obtained using the SMOTE technique with M.L.P. (Abou Elassad, Mousannif & Al Moatassime, 2020).…”

Section: Data Balancing Techniques In Crash Severity Prediction Modelingmentioning

confidence: 99%

An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

Ampadu

Haq

Ksaibati

2022

J. sustain. dev. transp. logist.

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This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.

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“…In today's modern world, the number of vehicles on roads has rapidly increased due to a surge in the population [1], resulting in an increased number of traffic accidents, especially in winter, when conditions making driving more difficult than in normal situations. Traffic accidents on roads are among the most dangerous and serious problems in society and can lead to detrimental impacts on both individuals and communities, resulting in fatalities, health issues, and economic losses [2]. Driving conditions during the winter in countries with cold climates, such as Norway, Sweden, Finland, and Canada, can be hazardous due to ice formation on the ground, snowfall, and poor visibility.…”

Section: A Motivationmentioning

confidence: 99%

Using Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance

Hatamzad¹,

Pinerez²,

Casselgren

2021

IEEE Access

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The prediction of efficiency scores for winter road maintenance (WRM) is a challenging and serious issue in countries with cold climates. While effective and efficient WRM is a key contributor to maximizing road transportation safety and minimizing costs and environmental impacts, it has not yet been included in intelligent prediction methods. Therefore, this study aims to design a WRM efficiency classification prediction model that combines data envelopment analysis and machine learning techniques to improve decision support systems for decision-making units. The proposed methodology consists of six stages and starts with road selection. Real data are obtained by observing road conditions in equal time intervals via road weather information systems, optical sensors, and road-mounted sensors. Then, data preprocessing is performed, and efficiency scores are calculated with the data envelopment analysis method to classify the decision-making units into efficient and inefficient classes. Next, the WRM efficiency classes are considered targets for machine learning classification algorithms, and the dataset is split into training and test datasets. A slightly imbalanced binary classification case is encountered since the distributions of inefficient and efficient classes in the training dataset are unequal, with a low ratio between classes. The proposed methodology includes a comparison of different machine learning classification techniques. The graphical and numerical results indicate that the combination of a support vector machine and genetic algorithm yields the best generalization performance. The results include analyzing the variables that affect the WRM and using efficiency classes to drive future insights to improve the process of decision-making.

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A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution

Cited by 44 publications

References 54 publications

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

Using Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance

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