Explainability techniques applied to road traffic forecasting using ...

Our approach provides a comprehensive combination of predictive and explainability techniques. Firstly, we compared statistical regression, classic machine ...

Of the great variety of deep learning models, the best predictive model in spatio-temporal traffic datasets was found to be the Adaptive Graph Convolutional ...

Explainability techniques applied to road traffic forecasting using ...

Request PDF | Explainability techniques applied to road traffic forecasting using Graph Neural Network models | In recent years, several new Artificial ...

Explainability techniques applied to road traffic forecasting using ...

Smith, Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches, с. · Graves, Speech recognition with ...

Explainability techniques applied to road traffic forecasting ... - RUA

Of the great variety of deep learning models, the best predictive model in spatio-temporal traffic datasets was found to be the Adaptive Graph Convolutional ...

Explainability techniques applied to road traffic forecasting using ...

In recent years, several new Artificial Intelligence methods have been developed to make models more explainable and interpretable. The techniques ...

Evaluating the Performance of Explainable Machine Learning ...

In this paper, we seek to explore the performance of explainable machine learning models applied to the prediction of road traffic crashes.

Explainable Traffic Flow Prediction with Large Language Models

Explainable predictions offer valuable insights into the factors influencing traffic patterns, which help urban planners, traffic engineers, and policymakers ...

A Note on the Explainability of Black-box Machine Learning Models ...

... The xAI method Shapley Additive exPlanation (SHAP) is used to extract knowledge from the two models that are used for traffic prediction in [26] ...

Speed prediction and nearby road impact analysis using machine ...

In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and LASSO to ...

Speed prediction and nearby road impact analysis using machine ...

In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and ...

A Note on the Explainability of Black-box Machine Learning Models ...

After the swerve towards Artificial Intelligence that gradually took place in the modeling sphere of traffic forecasting, predictive schemes have ever since ...

jwwthu/GNN4Traffic: This is the repository for the collection ... - GitHub

Explainability techniques applied to road traffic forecasting using Graph Neural Network models[J]. ... Traffic Speed Prediction Based on Time Classification in ...

A Fundamental Model with Stable Interpretability for Traffic Forecasting

Explainability techniques applied to road traffic forecasting using Graph. Neural Network models. Information Sciences (2023), 119320. [8] Shengnan Guo ...

An Overview Based on the Overall Architecture of Traffic Forecasting

The traffic prediction techniques were categorized into four groups in [18], including machine learning, computational intelligence, deep ...

[47]. García-Sigüenza J, Llorens-Largo F, Tortosa L, Vicent JF. 2023. Explainability techniques applied to road traffic forecasting using Graph Neural Network ...

Navigating your way: Traffic Prediction with Machine Learning

By meticulously refining and curating the dataset with these techniques, we can enhance the model's ability to discern patterns and improve the precision of ...

Towards Explainable Traffic Flow Prediction with Large Language ...

Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of ...

Multistep traffic forecasting by dynamic graph convolution

Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems.