Hourly Traffic Flow Prediction Using Different ANN Models

Topuz, Vedat

doi:10.5772/10177

Cited by 9 publications

(8 citation statements)

References 3 publications

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“…Since in many studies, the prediction performance of deep learning models is better than traditional statistical models and machine learning models, five prediction models based on deep learning including ANN [3], LSTM, DeepST [29], DNN-BTF [37] and AGCNN [38] are compared in the experiment to verify the dominance of the proposed ASA-RGCNN prediction model. The size of each input sample is (12,12,3).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

“…As a result, studies based on this 40 kind of approach is beginning to emerge. [3] used different artificial neural network (ANN) model to predict the hourly traffic flow in second tolled bridge of Bosphorus in Turkey, and proved the prediction quite accurate. [4] proposed a novel NN training method that employs the hybrid exponential smoothing method and the Levenberg Marquardt algorithm, which aims to improve the generalisation capabilities of previously used methods for training NNs for short‐term traffic flow forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

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A deep network with analogous self‐attention for short‐term traffic flow prediction

Zhang

Jiao

2021

IET Intelligent Trans Sys

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Short‐term traffic flow prediction plays a crucial role in research and application of intelligent transportation system. Neural network algorithm can use the big data for training and has more advantages over other prediction models in traffic features extraction. However, it is still a problem to extract the spatiotemporal features of traffic flow in a simple and sufficient way to improve the prediction accuracy. In this paper, a double‐branch deep residual gated convolutional neural network (RGCNN) is proposed to extract features from both time and space based on three‐dimensional traffic data, and scaled exponential linear units is used as an activation function to enhance the convergence effect of network training. In order to increase the ability of the network to fit the traffic data, an analogous self‐attention (ASA) is designed, which retains the advantages of attention while hardly increasing training costs. Simulation experiments are carried out in real traffic data sets, the simulation results of traffic flow prediction tasks in different prediction horizons show that the prediction performance of the proposed prediction model (ASA‐RGCNN) is superior to that of other common prediction models and the proposed model can be applied to the predicting task under different traffic conditions. By visualising ASA weights at different traffic flow levels, the impact of space‐time traffic data on the prediction task can also be found out.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A deep network with analogous self‐attention for short‐term traffic flow prediction

Zhang

Jiao

2021

IET Intelligent Trans Sys

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“…• Parallel (P) Mode: In this case, the estimated outputs are fed-back and the output regressor can be defined as [19]:…”

Section: A Narx Model Structurementioning

confidence: 99%

Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs

Zotti

Binder

Hansen

et al. 2020

2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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In power systems, electrical consumers can become a significant source of flexibility, by adjusting their consumption according to grid's needs while respecting their operational constraints. Consumers' flexibility potential can be exploited through the submission of dynamic electricity prices. Such prices are able to describe the variable condition of the power system and are broadcast to the consumers in order to obtain a certain change in consumption. The formulation of effective dynamic prices requires the development of proper models that describe the price responsiveness of electrical consumers. In this paper, we propose a nonlinear prediction model for the dynamic electricity prices in demand response (DR) programs. Specifically, the nonlinear autoregressive with exogenous input (NARX) model structure is used to learn from available data to predict appropriate electricity price signals. For the validation of the model (in an aggregate manner) in predicting consumers' price-response, the data from 10 Danish households is utilised, which has provided by the Danish Transmission Service Operator (TSO) Energinet.

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“…The most widely used ANN-based models in short-term traffic flow forecasting are multilayer perception (MLP), backpropagation neural networks (BPNN), and radial basis function neural networks (RBFNN) [ 10 ]. The pros and cons of these models have been addressed in the literature [ 7 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

Chen

Hung

2018

Computational Intelligence and Neuroscience

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Forecasting short-term traffic flow is a key task of intelligent transportation systems, which can influence the traveler behaviors and reduce traffic congestion, fuel consumption, and accident risks. This paper proposes a fuzzy wavelet neural network (FWNN) trained by improved biogeography-based optimization (BBO) algorithm for forecasting short-term traffic flow using past traffic data. The original BBO is enhanced by the ring topology and Powell's method to advance the exploration capability and increase the convergence speed. Our presented approach combines the strengths of fuzzy logic, wavelet transform, neural network, and the heuristic algorithm to detect the trends and patterns of transportation data and thus has been successfully applied to transport forecasting. Other different forecasting methods, including ANN-based model, FWNN-based model, and WNN-based model, are also developed to validate the proposed approach. In order to make the comparisons across different methods, the performance evaluation is based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R). The performance indexes show that the FWNN model achieves lower RMSE and MAPE, as well as higher R, indicating that the FWNN model is a better predictor.

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Hourly Traffic Flow Prediction Using Different ANN Models

Cited by 9 publications

References 3 publications

A deep network with analogous self‐attention for short‐term traffic flow prediction

A deep network with analogous self‐attention for short‐term traffic flow prediction

Data-driven Nonlinear Prediction Model for Price Signals in Demand Response Programs

Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based Optimization Algorithm

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