Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models

Chan, Kit Yan; Dillon, Tharam S.

doi:10.1109/ijcnn.2014.6889374

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…A five-layered dynamic FNN was utilized to perform traffic forecasting (Li, 2016). Unlike traditional FNNs, the network structure was generated during the training process.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Survey of neural network‐based models for short‐term traffic state prediction

Loan

Taherifar

2018

WIREs Data Min & Knowl

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Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN‐based approaches proposed for short‐term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in‐depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short‐term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks. This article is categorized under: Technologies > Prediction Technologies > Machine Learning Application Areas > Science and Technology

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“…A five-layered dynamic FNN was utilized to perform traffic forecasting (Li, 2016). Unlike traditional FNNs, the network structure was generated during the training process.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Survey of neural network‐based models for short‐term traffic state prediction

Loan

Taherifar

2018

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…Neural networks are also commonly used for various types of predictions (Kumar, Parida, & Katiyar, 2013;Lee et al, 2014;Ow, Ngo, & Lee, 2016). Others added neural network when using linear fuzzy for short time prediction on toll roads to design a number of sensors on the highway (Chan & Dillon, 2014). Other research used naive Bayes for predicting traffic congestion (Kim & Wang, 2016), which can also be combined with support vector regression (Ahn, 2016).…”

Section: Related Workmentioning

confidence: 99%

Traffic Flow Prediction Model Based on Neighbouring Roads Using Neural Network and Multiple Regression

Priambodo

Ahmad

2018

JICT

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Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays and weekends to achieve more accurate results. We exploit similarity of traffic flow pattern on weekdays and weekends by adding

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Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression

Priambodo

Ahmad

2017

Advances in Visual Informatics

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Traffic flow prediction using orthogonal arrays and Takagi-Sugeno neural fuzzy models

Cited by 4 publications

References 17 publications

Survey of neural network‐based models for short‐term traffic state prediction

Survey of neural network‐based models for short‐term traffic state prediction

Traffic Flow Prediction Model Based on Neighbouring Roads Using Neural Network and Multiple Regression

Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression

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