A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting

Cui, Zhihan; Huang, Boyu; Dou, Haowen; Cheng, Yan Ming; Guan, Jitian; Zhou, Teng

doi:10.3390/math10122087

Cited by 13 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, there are several studies in the literature that focus on optimising the network model to improve the immunity of the model to noise. For example, Cui et al [59] proposed a two-stage hybrid learning model to search the initial parameter values of the GSA in a data-driven manner with the PSO algorithm to improve the efficiency of the global optimum search. Yin et al [60] introduced a modified GSA with crossover (CROGSA), where the crossover-based search scheme utilises the promising knowledge extracted from the currently obtained global optimum positions to improve the exploitation capabilities.…”

Section: Discussionmentioning

confidence: 99%

Gravitational search algorithm‐extreme learning machine for COVID‐19 active cases forecasting

et al. 2023

Self Cite

View full text Add to dashboard Cite

Corona Virus disease 2019 (COVID-19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non-pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID-19 in some countries, it has been criticized as inefficient or ineffective, because of the time-delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA-ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID-19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID-19 dataset from Johns Hopkins University, the MAPE of the authors' model is 7.79%, which corroborates the superiority of the model to state-of-the-art methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Gravitational search algorithm‐extreme learning machine for COVID‐19 active cases forecasting

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods include the exponential smoothing model (ES), grey model (GM), least‐squares boosting (LSBOOST), support vector regression method (SVR), stacked autoencoder model (SAE), Kalman filtering model (KF), and LSTM model. Besides, six latest state‐of‐the‐art models are included, SVRGSAS [19], SrOrkNNr [46], GA‐KELM [21], PSOGSA‐ELM [47], ABC‐ELM [48], NiLSTM [20]. The brief introductions of these models are as follows.…”

Section: Case Studymentioning

confidence: 99%

“…The emerging machine learning methods have been employed for short‐term traffic flow forecasting, such as deep belief network [14], fuzzy logic [15], Kalman filter [16, 17], ensemble learning [18], support vector regression [19], k ‐nearest neighbor [20], and extreme learning machines [21]. Machine learning methods require a large amount of sample data and sufficient training effort to establish the mapping function [22, 23].…”

Section: Introductionmentioning

confidence: 99%

Lag‐related noise shrinkage stacked LSTM network for short‐term traffic flow forecasting

Li,

Bai,

Huang

et al. 2023

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

For the transport networks only equipped with sparse or isolated detectors, short‐term traffic flow forecasting faces the following problems: (1) there are only temporal information and no spatial information; (2) the noises in the traffic flow significantly affect the forecasting performance. In this paper, a lag‐related noise shrinkage stacked long short‐term memory (LSTM) network is proposed for the traffic flow forecasting task only related to temporal information. To extract effective temporal features, the optimal time lags are selected in the traffic flow and converted into lag‐related multi‐dimensional data. Then, a discrete wavelet threshold denoising shrinkage algorithm is designed to filter the noises to construct a more reliable training set. A multi‐level stacked LSTM network is employed to learn the features of the training set to map the past traffic flow to the future flow. Four benchmark datasets are to evaluate the forecasting performance by extensive experiments. The comparison with the state‐of‐the‐art models demonstrates an average improvement of 7.28% in MAPE and 6.02% in RMSE. In addition, the proposed method has been applied in the Guilin Travel Network Bus Intelligent Dispatching System. It improves the utilization of the vehicles and reduces operating costs.

show abstract

“…In recent decades, researchers have proposed a variety of short-term traffic forecasting models differing in complexity, methodology, and performances [9][10][11][12][13]. The researchers used different traffic flow parameters to propose efficient models in different scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

et al. 2022

View full text Add to dashboard Cite

Nowadays, short-term traffic flow forecasting has gained increasing attention from researchers due to traffic congestion in many large and medium-sized cities that pose a serious threat to sustainable urban development. To this end, this research examines the forecasting performance of functional time series modeling to forecast traffic flow in the ultra-short term. An appealing feature of the functional approach is that unlike other methods, it provides information over the whole day, and thus, forecasts can be obtained for any time within a day. Within this approach, a Functional AutoRegressive (FAR) model is used to forecast the next-day traffic flow. For empirical analysis, the traffic flow data of Dublin airport link road, Ireland, collected at a fifteen-minute interval from 1 January 2016 to 30 April 2017, are used. The first twelve months are used for model estimation, while the remaining four months are for the one-day-ahead out-of-sample forecast. For comparison purposes, a widely used model, namely AutoRegressive Integrated Moving Average (ARIMA), is also used to obtain the forecasts. Finally, the models’ performances are compared based on different accuracy statistics. The study results suggested that the functional time series model outperforms the traditional time series models. As the proposed method can produce traffic flow forecasts for the entire next day with satisfactory results, it can be used in decision making by transportation policymakers and city planners.

show abstract

A Two-Stage Hybrid Extreme Learning Model for Short-Term Traffic Flow Forecasting

Cited by 13 publications

References 49 publications

Gravitational search algorithm‐extreme learning machine for COVID‐19 active cases forecasting

Gravitational search algorithm‐extreme learning machine for COVID‐19 active cases forecasting

Lag‐related noise shrinkage stacked LSTM network for short‐term traffic flow forecasting

Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

Contact Info

Product

Resources

About