Generalized Regression Neural Network for predicting traffic flow

Buliali, Joko Lianto; Hariadi, Victor; Saikhu, Ahmad; Mamase, Saprina

doi:10.1109/icts.2016.7910298

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…The results showed the inefficiency of Webster's method to handle oversaturated traffic. Ali et al [114] combined fuzzy logic and the Webster optimum cycle formula, showing an increase of the average waiting time by (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)% relative to MaxPressure and fixedtime, respectively. Considering intersection delay, fuel consumption levels, and emissions, Calle-Laguna et al [115] applied Webster's method to estimate the optimum cycle length and eventually found an overestimation of the method.…”

Section: Webster's Methodsmentioning

confidence: 99%

“…Park et al [28] used an RBFNN for short-term freeway traffic volume prediction, with results topping around 64.81% and 91.39%, with forecast traffic volumes being in the 10% and 20% error range, respectively. The prediction accuracy was improved by combining fuzzy C-means with an RBFNN and using a Generalized Regression Neural Network (GRNN) following the work of Park [29], Kuang et al [30], and Buliali et al [31]. On top of the GRNN, Buliali et al used a Leave-One-Out Cross-Validation (LOOCV) method to determine the suitable smoothing factor in order to to avoid overfitting, achieving an RMSE of 16.4.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

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Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

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Section: Webster's Methodsmentioning

confidence: 99%

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For a small number of sample data, GRNN also has a good prediction effect, and it can be used to deal with unstable data. The three ANNs have been widely used in data modeling and prediction [30]- [31]. Raza and Mithulananthan et al [32] used the Elman network to build an ensemble forecast framework (ENFF) for demand prediction of anomalous days.…”

Section: Model Buildingmentioning

confidence: 99%

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

Zhi

Liu

et al. 2020

IEEE Access

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The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this paper, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R 2 (R-squared) increased to 0.9843. What's more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.

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“…This model demonstrated its better approximation capability by outperforming RBFNN and BPNN in the conducted experiments. A similar model to the GRNN model of Leng et al (2013) was also applied and achieved promising results (Buliali, Hariadi, Saikhu, & Mamase, 2016).…”

Section: Radial Basis Function 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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Generalized Regression Neural Network for predicting traffic flow

Cited by 6 publications

References 5 publications

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

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

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