ArticleEntropy (Basel, Switzerland)2022
Entropy-Weight-Method-Based Integrated Models for Short-Term Intersection Traffic Flow Prediction.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Entropy and Normalization in MCDA: A Data-Driven Perspective on Ranking Stability.Entropy (Basel, Switzerland) · 2026Article
- Spatiotemporal dynamics and forecasting of public attention to entrepreneurship education: An entropy-based modeling approach.PloS one · 2026Article
- Multi-Objective Optimization of Dental Implant Designs With Multi-Recessed Holes: Insights From Static and Dynamic Finite Element Analysis.International journal for numerical methods in biomedical engineering · 2025Article
- Classification of Sleeping Position Using Enhanced Stacking Ensemble Learning.Entropy (Basel, Switzerland) · 2024Article
- Materials and Products Development Based on a Novelty Approach to Quality and Life Cycle Assessment (QLCA).Materials (Basel, Switzerland) · 2024Article
- Quality evaluation of chicken soup based on entropy weight method and grey correlation degree method.Scientific reports · 2024Article
- Article
- The Impact of the Intuitionistic Fuzzy Entropy-Based Weights on the Results of Subjective Quality of Life Measurement Using Intuitionistic Fuzzy Synthetic Measure.Entropy (Basel, Switzerland) · 2023Article
- Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.PloS one · 2022Article
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Authors and funding
9 authors.
Funding
Abstract
Three different types of entropy weight methods (EWMs), i.e., EWM-A, EWM-B, and EWM-C, have been used by previous studies for integrating prediction models. These three methods use very different ideas on determining the weights of individual models for integration. To evaluate the performances of these three EWMs, this study applied them to developing integrated short-term traffic flow prediction models for signalized intersections. At first, two individual models, i.e., a k-nearest neighbors (KNN)-algorithm-based model and a neural-network-based model (Elman), were developed as individual models to be integrated using EWMs. These two models were selected because they have been widely used for traffic flow prediction and have been approved to be able to achieve good performance. After that, three integrated models were developed by using the three different types of EWMs. The performances of the three integrated models, as well as the individual KNN and Elman models, were compared. We found that the traffic flow predicted with the EWM-C model is the most accurate prediction for most of the days. Based on the model evaluation results, the advantages of using the EWM-C method were deliberated and the problems with the EWM-A and EWM-B methods were also discussed.
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