Cooperative Incident Management in Mixed Traffic of CAVs and Human-Driven Vehicles

Yue, Wenwei; Li, Changle; Wang, Shangbo; Xue, Nan; Wu, Jin−Jei

doi:10.1109/tits.2023.3289983

Cited by 36 publications

(8 citation statements)

References 39 publications

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“…Modal aliasing could be further reduced by using adaptive white noise to increase decomposition effectiveness. It is possible to utilize CEEMDAN to analyze and handle non-stationary signals in time and frequency 59 . It can generate signal wave patterns of various sizes and create a series of data sequences with local features at various periods, where each component is a stationary IMF.…”

Section: Methodsmentioning

confidence: 99%

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Guermoui,

Fezzani,

Mohamed

et al. 2024

Sci Rep

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Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply–demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454–1.54] for the three studied PV stations.

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Section: Methodsmentioning

confidence: 99%

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Guermoui,

Fezzani,

Mohamed

et al. 2024

Sci Rep

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“…The RF classifier was chosen because of its exceptional ability to correctly categorize instances of driving behavior. Its ensemble nature, which combines several DTs, enables it to identify intricate links and patterns in the data and average their predictions for the final classifier, given in Equation (19). With this method, the predictive performance of the system is improved, and it offers insightful information and trustworthy results for the study of driver behavior analysis.…”

Section: Random Forest Classifiermentioning

confidence: 99%

“…Moreover, up to 96% of crashes are attributed, at least partially, to driver mistakes [17]. Consequently, improving human variables that influence dangerous driving behaviors is crucial for creating effective treatments to reduce the likelihood of crashes and increase road safety [18,19].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques

Alam,

Wang,

Pervez

et al. 2024

Sustainability

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Driver behavior plays a pivotal role in ensuring road safety as it is a significant factor in preventing traffic crashes. Although extensive research has been conducted on this topic in developed countries, there is a notable gap in understanding driver behavior in developing countries, such as Pakistan. It is essential to recognize that the cultural nuances, law enforcement practices, and government investments in traffic safety in Pakistan are significantly different from those in other regions. Recognizing this disparity, this study aims to comprehensively understand risky driving behaviors in Peshawar, Pakistan. To achieve this goal, a Driver Behavior Questionnaire was designed, and responses were collected using Google Forms, resulting in 306 valid responses. The study employs a Fuzzy Analytical Hierarchy Process framework to evaluate driver behavior’s ranking criteria and weight factors. This framework assigns relative weights to different criteria and captures the uncertainty of driving thought patterns. Additionally, machine learning techniques, including support vector machine, decision tree, Naïve Bayes, Random Forest, and ensemble model, were used to predict driver behavior, enhancing the reliability and accuracy of the predictions. The results showed that the ensemble machine learning approach outperformed others with a prediction accuracy of 0.84. In addition, the findings revealed that the three most significant risky driving attributes were violations, errors, and lapses. Certain factors, such as clear road signage and driver attention, were identified as important factors in improving drivers’ risk perception. This study serves as a benchmark for policymakers, offering valuable insights to formulate effective policies for improving traffic safety.

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“…Many researchers have attempted to investigate accident-contributing elements; however, little work has been given to explaining black box models [ 27 , 28 ]. The authors applied five machine learning models and explainable machine learning [ 29 , 30 ]. The primary goal of this research is to develop an accident injury severity prediction model based on a transfer learning approach and to identify major contributing elements utilizing an explainable approach.…”

Section: Introductionmentioning

confidence: 99%

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Aboulola

2024

PLoS ONE

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Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

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Cooperative Incident Management in Mixed Traffic of CAVs and Human-Driven Vehicles

Cited by 36 publications

References 39 publications

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

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