Predicting interstate motor carrier crash rate level using classification models

Liu, Jundi; Boyle, Linda N.; Banerjee, Ashis G.

doi:10.1016/j.aap.2018.06.005

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…Once government policies are developed, regulatory bodies must ensure proper and efficient identification of high‐risk drivers and carriers (Hwang, Boyle, and Banerjee 2019; Lantz and Loftus 2005; Liu, Boyle, and Banerjee 2018), and meaningful, consistent enforcement through efforts such as compliance reviews (Chen 2008). Audits can improve the safety standard of the industry, but in order to be effective penalties must be high (when probability of enforcement is small), or the probability of being caught must be high (when the penalty is small; Moses and Savage 1992).…”

Section: Report the Results (Step 6)mentioning

confidence: 99%

Motor-Carrier Safety: A Review and Research Recommendations for 2020 and Beyond

Douglas

2021

Transportation Journal

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This article presents results of a systematic review of the US motor-carrier safety literature in transportation, logistics, and safety journals. The discipline has seen growth in research over the decades, and growth of the field rapidly increased in the last decade. We organize the literature into a systems framework and summarize the research across industry system levels to include government, regulators, carriers, and drivers. We then apply a goal-framing approach to reveal some of the dynamic interactions between system levels and the environment, as entities work to minimize risk to life and property during freight operations while striking a balance between the industry's welfare and societal welfare. This article provides recommendations for future research to fill gaps in the current body of knowledge and to aid government officials, regulators and law enforcement officials, carrier managers, and drivers in addressing industry challenges and maintaining safe roads in 2020 and beyond.

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Section: Report the Results (Step 6)mentioning

confidence: 99%

Motor-Carrier Safety: A Review and Research Recommendations for 2020 and Beyond

Douglas

2021

Transportation Journal

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“…In the second procedure, the neural network model is estimated with all the selected variables as inputs, and the relative importance of every input variable is calculated. As a nonlinear model, determining the relative importance of variables is more difficult than in linear regression models, and Garson's method is used in this study, because it was proved that the neural network can identify the most influential input variables from a given variable list by Garson's method (Garson 1991), and recently, the reliability of Garson's method in the variable selection process of three-layer neural network is verified to be better than other widely used methods, such as correlation method and principal component analysis (Papatheocharous and Andreou 2010;Fischer 2015;Yousefi et al 2018;Liu et al 2018). According to Garson's method (Garson 1991), for a neural network model with N neurons in the input layer and L neurons in the hidden layer, the relative importance of the ith input variable to the kth output variable ( I ik ) can be defined as where ij is the weight of the ith neuron in the input layer and jth neuron in the hidden layer, and jk is the weight of the jth neuron in the hidden layer and kth neuron in the output layer.…”

Section: Neural Network Model Selection Processmentioning

confidence: 99%

Neural network model for predicting variation in walking dynamics of pedestrians in social groups

Sun

Duives

et al. 2022

Transportation

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“…The F1-score is a harmonized average of precision and recall that can accurately evaluate the model's performance when the data label is imbalanced. The larger the F1-score, the better the model can be determined, and the calculation formula is presented in Equation ( 5) [41,42]. From Table 3, it can be seen that the average of the overall classification accuracy for all scenarios is 81%.…”

Section: Derivation Of the Risk Level Of Accident Severity Classification Based On The Ann (Stage 2)mentioning

confidence: 99%

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data

Lee

Kim

Park

et al. 2020

IJERPH

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Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

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Predicting interstate motor carrier crash rate level using classification models

Cited by 14 publications

References 11 publications

Motor-Carrier Safety: A Review and Research Recommendations for 2020 and Beyond

Motor-Carrier Safety: A Review and Research Recommendations for 2020 and Beyond

Neural network model for predicting variation in walking dynamics of pedestrians in social groups

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data

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