Tunable and Transferable RBF Model for Short-Term Traffic Forecasting

Cai, Pinlong; Wang, Yunpeng; Lu, Guangquan

doi:10.1109/tits.2018.2882814

Cited by 17 publications

(3 citation statements)

References 39 publications

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“…Jia and Tian [21] proposed a short-term power load prediction model based on the fuzzy RBF neural network, which overcomes the shortcomings of the BP algorithm such as slow convergence speed and easiness of falling into local minimum. Cai et al [22] introduced a tunable, transferable radial basis function (TT-RBF) model for online prediction based on the RBF neural network. The RBF model is a global-oriented interpolation algorithm, which is particularly applicable for small sample prediction problems.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Jia²,

Mei

et al. 2020

IEEE Access

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With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as ''road speed'') is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems. INDEX TERMS Urban traffic flow, speed prediction, fuzzy C-means, fuzzy-RBF, neural network.

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Section: Introductionmentioning

confidence: 99%

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Jia²,

Mei

et al. 2020

IEEE Access

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“…( e RBF neural network model is a feedforward neural network, and it has strong nonlinear mapping ability and can approximate a nonlinear function with arbitrary precision [29,30]. erefore, it is more suitable for the prediction of random terms in time series.…”

Section: Prophet Modelmentioning

confidence: 99%

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Liu

Jiao

Sun

et al. 2021

Scientific Programming

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The South China Sea is China’s largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea.

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“…Compared with other neural networks, it has the following merits: a strong approximation ability, simple network structure, and rapid learning speed [39,40]. It has been widely used in short-term traffic volume forecasting [41], groundwater level forecasting [42], water demand forecasting [43], and many other fields [44]. Nevertheless, it is rarely used in the field of ecosystem assessment [45,46].…”

Section: Introductionmentioning

confidence: 99%

Exploring Development Trends of Terrestrial Ecosystem Health—A Case Study from China

Zhang

et al. 2021

Land

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Terrestrial ecosystem health (TEH) is the basis of regional sustainability development. The state of TEH is an important research direction in the land science field. The purpose of this paper was to explore the development trends and influencing factors of the. By using the radial basis function (RBF), neural network model, geographic information system (GIS), and the comprehensive index method, this paper predicted the land ecological changes of Henan Province from 2007 to 2025 based on a comprehensive evaluation of the system. The results show that the TEH of Henan Province exhibited a general trend of improvement from 2007 to 2025. The predictions exhibited a tendency to fluctuate and increase, from “severe warning” to “moderate warning” and even to “no warning” state. The early warning index of the subsystem showed a fluctuating upward trend except for the press subsystem, which fluctuated between “extraordinary warning” and “heavy warning” states. The overall TEH level is improving but is largely dependent on effective corresponding measures. The health status of the land ecosystem in Henan Province is guaranteed to be stable due to improvements in rural residential incomes, mechanization levels of cultivated land, domestic sewage treatment rates, and the numbers of scientific and technological personnel per unit of land. The TEH is mainly restricted by the population densities, urbanization levels, inputs of fertilizers and pesticides, and average wastewater load factors of the land. To improve the health level of the land ecosystem, it is necessary to reduce the use of fertilizers and pesticides and to control the urbanization rate. At the same time, improving the level of forest coverage and the effective irrigation rate play a positive role in improving ecosystem health. The results provide a reference for land-use planning and management decisions.

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Tunable and Transferable RBF Model for Short-Term Traffic Forecasting

Cited by 17 publications

References 39 publications

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Exploring Development Trends of Terrestrial Ecosystem Health—A Case Study from China

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