Predicting Red Light Running Violation Using Machine Learning Classifiers

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

“…It is often used for regression and classification problems. The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification) 45 . The ensemble method employs many learning models to provide a higher prediction result.…”

“…The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification). 45 The ensemble method employs many learning models to provide a higher prediction result. In the instance of RF, the model produces a whole forest of random uncorrelated decision trees to reach the best feasible solution.…”

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

“…It is often used for regression and classification problems. The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification) 45 . The ensemble method employs many learning models to provide a higher prediction result.…”

“…The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification). 45 The ensemble method employs many learning models to provide a higher prediction result. In the instance of RF, the model produces a whole forest of random uncorrelated decision trees to reach the best feasible solution.…”

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

“…Arc map software (version 9.3), developed by the Environmental Systems Research Institute headquartered in Redlands, California, was used for spatial analysis [63]. The software has become popular for various transportation applications [64][65][66][67]. The software was also used to locate and eliminate false outliers in the dataset.…”

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

“…In order to reduce the frequency of RLR at its source, it is necessary to identify RLR behavior in advance. Some studies have taken vehicle trajectory data, video detector data, connected vehicles data, etc., as a basis for training machine learning models or deep learning models for RLR prediction [29][30][31][32][33][34][35]. However, owing to the scarcity of RLR vehicle data, these models are difficult to train and hard to apply in practice.…”

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

“…It is often used for regression and classification problems. The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification) 45 . The ensemble method employs many learning models to provide a higher prediction result.…”

“…The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification). 45 The ensemble method employs many learning models to provide a higher prediction result. In the instance of RF, the model produces a whole forest of random uncorrelated decision trees to reach the best feasible solution.…”

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

“…Arc map software (version 9.3), developed by the Environmental Systems Research Institute headquartered in Redlands, California, was used for spatial analysis [63]. The software has become popular for various transportation applications [64][65][66][67]. The software was also used to locate and eliminate false outliers in the dataset.…”

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

“…In order to reduce the frequency of RLR at its source, it is necessary to identify RLR behavior in advance. Some studies have taken vehicle trajectory data, video detector data, connected vehicles data, etc., as a basis for training machine learning models or deep learning models for RLR prediction [29][30][31][32][33][34][35]. However, owing to the scarcity of RLR vehicle data, these models are difficult to train and hard to apply in practice.…”

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

“…It is often used for regression and classification problems. The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification) 45 . The ensemble method employs many learning models to provide a higher prediction result.…”

“…The RF algorithm works by aggregating decision trees during training and outputs the mean prediction (regression) of the particular trees' leverage or class, which is the mode of the classes (classification). 45 The ensemble method employs many learning models to provide a higher prediction result. In the instance of RF, the model produces a whole forest of random uncorrelated decision trees to reach the best feasible solution.…”

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

“…Arc map software (version 9.3), developed by the Environmental Systems Research Institute headquartered in Redlands, California, was used for spatial analysis [63]. The software has become popular for various transportation applications [64][65][66][67]. The software was also used to locate and eliminate false outliers in the dataset.…”

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

“…In order to reduce the frequency of RLR at its source, it is necessary to identify RLR behavior in advance. Some studies have taken vehicle trajectory data, video detector data, connected vehicles data, etc., as a basis for training machine learning models or deep learning models for RLR prediction [29][30][31][32][33][34][35]. However, owing to the scarcity of RLR vehicle data, these models are difficult to train and hard to apply in practice.…”