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predicao-de-dados-de-fluxo-de-trafego's Introduction

Trabalhos primários obtidos através do trabalho "Predição de Dados de Fluxo de Tráfego: Uma Revisão Sistemática"

Na tabela abaixo, é possível visualizar os trabalhos primários selecionados após aplicação dos critérios de inclusão e exclusão. Para cada trabalho, pode-se visualizar a avaliação da qualidade, as técnicas de predição e de pré-processamento e as métricas utilizadas para validação dos modelos.

Para ter acesso ao texto completo, basta clicar na referência na tabela.

Estudos Qualidade
da
Execução
Adequação
ao
Objetivo
Adequação
ao
Contexto
Ranqueamento Técnicas
de
Predição
Técnicas
de
Pré-processamento
Métricas
de
Validação
Aljuaydi et al. [2022] Alta Alta Alta Alta CNN, LSTM, Autoencoder LSTM Normalização RMSE, MAE
An et al. [2019] Alta Alta Alta Alta Fuzzy-based CNN Normalização, Preenchimento de dados faltosos MSE, MAE, RMSE
Awan et al. [2021] Alta Alta Alta Alta CNN, LSTM Normalização MAPE, RMSE
Bao et al. [2021] Média Média Alta Média DBN, SVR Normalização Accuracy, Mean Computing Time
Bartlett et al. [2019] Alta Média Alta Média CNN, GRU Não informado RMSE
Bilotta et al. [2022] Alta Alta Alta Alta CONV-BI-LSTM Normalização MAE, MAPE, RMSE, MASE
Buroni et al. [2021] Alta Média Alta Média FNN, GCN, GRU Não informado RMSE, MAE, MASE
Cai et al. [2020] Alta Média Média Média PSO Correção de dados errôneos MAPE, RMSE
Cao et al. [2020] Alta Alta Alta Alta CNN, LSTM Wavelet Transform MAPE, RMSE
Chen et al. [2018] Alta Alta Alta Alta LSSVR Não informado RMSE
Chen et al. [2019] Alta Média Alta Média TFDC Normalização RMSE, MRE, MAE
Chen et al. [2021] Alta Média Alta Média LSTM Normalização, Remoção de dados anormais RMSE, MAE, MAPE
Chen et al. [2022] Alta Média Média Média RF, GWN Não informado RMSE, MAE
Djenouri et al. [2023] Alta Alta Alta Alta GCN, branch-and-bound LOF mAP
Duan et al. [2018] Alta Média Alta Média CNN, LSTM Não informado MSE, RMSE
Duan et al. [2019a] Alta Alta Alta Alta CPPBTR Não informado MAPE, RMSE
Duan et al. [2019b] Alta Baixa Baixa Baixa Convolutional LSTM Normalização MAPE, RMSE, MAE
Feng et al. [2022] Alta Alta Alta Alta GCN, GRU Normalização RMSE, MAE, Accuracy, R-square
Guo et al. [2019] Alta Média Alta Média SVR, LSTM Não informado MAPE, RMSE, MAE
Huang et al. [2019] Alta Média Média Média LSTM, GAV Não informado MAPE, RMSE
Hussain et al. [2021] Alta Alta Alta Alta GRU Não informado RMSE, MAPE, MAE
Jin et al. [2019] Alta Média Média Média GRU Não informado RMSE, MRE, MAE
Kong et al. [2020] Alta Alta Alta Alta STGAT Normalização MAE, RMSE, MAPE
Li et al. [2019] Alta Alta Média Média Densely CNN, LSTM Normalização RMSE
Li et al. [2020] Alta Alta Alta Alta Fuzzy Comprehensive Evaluation, LSTM-SPRVM Normalização MAPE, RMSE
Li et al. [2021a] Alta Alta Alta Alta DGCN Normalização MAE, MAPE, RMSE
Li et al. [2021b] Alta Alta Alta Alta CNN, LSTM. Wavelet Transform, Normalização RMSE, MAE, R-square
Liu et al. [2019a] Alta Média Média Média 3D-CNN, LSTM Normalização MPE, MER, PEV
Liu et al. [2019b] Alta Alta Alta Alta KELM Wavelet Transform RMSE, MAPE, R square
Ma et al. [2020] Alta Alta Alta Alta CNN, LSTM Normalização MAE, RMSE, NSE, CORR
Mena-Oreja e Gozalvez [2021] Alta Média Alta Média eRCNN Não informado MAE, MAPE, RMSE
Mou et al. [2019] Alta Alta Alta Alta LSTM Normalização RMSE, MAPE
Muhammed et al. [2018] Alta Média Média Média Stacked LSTM Normalização RMSE, MAE
Nigam e Srivastava [2023] Alta Média Alta Média CNN, LSTM Normalização MAE, RMSE
Olayode et al. [2021] Alta Alta Alta Alta RNA Não informado MAE, RMSE, R
Pranolo et al. [2022] Alta Média Média Média LSTM, PSO, Bifold-Attention Normalização MAPE, RMSE
Qi et al. [2020] Alta Alta Média Alta NCAE, ELM Normalização MAPE, VAPE
Reza et al. [2022] Alta Alta Alta Alta Multi-head Attention-based Transformer Normalização MAPE, MSE
Ruan et al. [2020] Alta Média Alta Média TCN Normalização MAE, MAPE, RMSE
Ruan et al. [2021] Alta Alta Alta Alta LSTM Normalização RMSE, MAE
Van Der Bijl et al. [2022] Alta Alta Alta Alta TBATS, SARIMAX, LSTM Não informado MAE
Villarroya et al. [2022] Alta Alta Alta Alta LSTM Não informado MAPE, sMAPE
Yang et al. [2020] Alta Média Alta Alta Attention Neural Network (DNN-Attention), Deep FM Não informado MAE, RMSE
Zang et al. [2019] Alta Alta Alta Alta RDBDGN Normalização MRE, MAE, RMSE
Zhang e Xin [2020] Alta Alta Alta Alta LSTM AGACS MAE, MRE, RMSE
Zhang et al. [2018] Alta Alta Alta Alta SVR, GA, RF Normalização MAPE, RMSE
Zhang et al. [2019] Média Alta Média Média LSTM, K-means clustering Não informado RMSE, MAE, R-square
Zhang et al. [2020a] Alta Alta Alta Alta MTL, GRU Normalização MAPE
Zhang et al. [2020b] Média Média Média Média RBM, SVR Normalização MAPE, RMSE
Zhang et al. [2021] Alta Média Alta Média CNN, GRU, Convolutional LSTM Não informado MAPE, MAE, RMSE
Zhao et al. [2019] Alta Alta Alta Alta TCN Não informado MAE, MRE
Zhao et al. [2020] Alta Média Média Média SAEs, LSTM, GRU Imputação de dados ausentes MAE, MRE, RMSE
Zheng e Huang [2020] Alta Alta Alta Alta LSTM Normalização MAE, MAPE, RMSE
Zheng et al. [2022] Alta Alta Alta Alta GCN, GAN Não informado MAE, MSE, RMSE
Zhu et al. [2019] Alta Média Média Média SVM, DBN Normalização MAPE, RMSE, MAE, MSE

Referências dos Trabalhos em Ordem Alfabética

Aljuaydi, F., Wiwatanapataphee, B., e Wu, Y. H. (2022).Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events.Alexandria Engineering Journal. ISSN 11100168.

An, J., Fu, L., Hu, M., Chen, W., e Zhan, J. (2019). A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information.IEEE Access, 7:20708–20722.

Awan, N., Ali, A., Khan, F., Zakarya, M., Alturki, R., Kundi, M., Alshehri, M. D., e Haleem, M. (2021).Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction. IEEE Access, 9:26502–26511. ISSN 2169-3536.

Bao, X., Jiang, D., Yang, X., e Wang, H. (2021).An improved deep belief network for traffic prediction considering weather factors.Alexandria Engineering Journal, 60(1):413–420. ISSN 11100168. URLhttps://doi.org/10.1016/j.aej.2020.09.003.

Bartlett, Z., Han, L., Nguyen, T. T., e Johnson, P. (2019).A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow.IEEE Access, 7: 153533–153541.

Bilotta, S., Collini, E., Nesi, P., e Pantaleo, G. (2022). Short-term prediction of city traffic flow via convolutional deep learning.IEEE Access, 10:113086–113099. ISSN 21693536.

Buroni, G., Lebichot, B., e Bontempi, G. (2021). Ast-mtl: An attention-based multi-task learning strategy for traffic forecasting.IEEE Access, 9:77359–77370. ISSN 21693536.

Cai, W., Yang, J., Yu, Y., Song, Y., Zhou, T., e Qin, J. (2020). PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting.IEEE Access, 8:6505–6514.

Cao, J., Guan, X., Zhang, N., Wang, X., e Wu, H. (2020). A Hybrid Deep Learning-Based Traf- fic Forecasting Approach Integrating Adjacency Filtering and Frequency Decomposition.IEEE Access, 8:81735–81746.

Chen, X., Cai, X., Liang, J., e Liu, Q. (2018). Ensemble Learning Multiple LSSVR With Improved Harmony Search Algorithm for Short-Term Traffic Flow Forecasting. IEEE Access, 6:9347–9357

Chen, C., Xu, Y., Zhao, J., Chen, L., e Xue, Y. (2022).Combining random forest and graph wavenet for spatial-temporal data prediction.Intelligent and Converged Networks, 3:364–377. ISSN 2708-6240. URLhttps://ieeexplore.ieee.org/document/10026523/.

Chen, W., An, J., Li, R., e Xie, G. (2019).Tensor-Train Fuzzy Deep Computation Model for Citywide Traffic Flow Prediction.IEEE Access, 7:120581–120593. ISSN 21693536. Chen, X., Cai, X., Liang, J., e Liu, Q. (2018). Ensemble Learning Multiple LSSVR With Improved Harmony Search Algorithm for Short-Term Traffic Flow Forecasting.IEEE Access, 6:9347– 9357.

Chen, Z., Wu, B., Li, B., e Ruan, H. (2021).Expressway Exit Traffic Flow Prediction for ETC and MTC Charging System Based on Entry Traffic Flows and LSTM Model.IEEE Access, 9: 54613–54624. ISSN 2169-3536.

Djenouri, Y., Belhadi, A., Srivastava, G., e Lin, J. C. W. (2023). Hybrid graph convolution neu- ral network and branch-and-bound optimization for traffic flow forecasting.Future Generation Computer Systems, 139:100–108. ISSN 0167739X.

Dresch, A., Lacerda, D. P., e J ́unior, J. A. V. A. (2015).Design science research: m ́etodo de pesquisa para avanc ̧o da ciˆencia e tecnologia. Bookman Editora.

Duan, W., Jiang, L., Wang, N., e Rao, H. (2019a). Pre-Trained Bidirectional Temporal Representa- tion for Crowd Flows Prediction in Regular Region.IEEE Access, 7:143855–143865.

Duan, Z., Yang, Y., Zhang, K., Ni, Y., e Bajgain, S. (2018). Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data.IEEE Access, 6:31820–31827.

Duan, Z., Zhang, K., Chen, Z., Liu, Z., Tang, L., Yang, Y., e Ni, Y. (2019b). Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time.IEEE Access, 7:127816–127832.

Feng, S., Huang, J., Shen, Q., Shi, Q., e Shi, Z. (2022). A hybrid model integrating local and global spatial correlation for traffic prediction.IEEE Access, 10:2170–2181. ISSN 21693536. Guo, J., Xie, Z., Qin, Y., Jia, L., e Wang, Y. (2019). Short-Term Abnormal Passenger Flow Predic- tion Based on the Fusion of SVR and LSTM.IEEE Access, 7:42946–42955.

Guo, J., Xie, Z., Qin, Y., Jia, L., e Wang, Y. (2019). Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM. IEEE Access, 7:42946–42955.

Huang, Z., Li, Q., Li, F., e Xia, J. (2019). A Novel Bus-Dispatching Model Based on Passenger Flow and Arrival Time Prediction.IEEE Access, 7:106453–106465.

Hussain, B., Afzal, M. K., Ahmad, S., e Mostafa, A. M. (2021). Intelligent traffic flow prediction using optimized gru model.IEEE Access, 9:100736–100746. ISSN 21693536.

Jin, Y., Jia, Z., Wang, P., Sun, Z., Wen, K., e Wang, J. (2019). Quantitative Assessment on Truck- Related Road Risk for the Safety Control via Truck Flow Estimation of Various Types.IEEE Access, 7:88799–88810. ISSN 21693536.

Kong, X., Xing, W., Wei, X., Bao, P., Zhang, J., e Lu, W. (2020). STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting.IEEE Access, 8:134363–134372.

Li, G., Knoop, V. L., e van Lint, H. (2021a). Multistep traffic forecasting by dynamic graph convolu- tion: Interpretations of real-time spatial correlations.Transportation Research Part C: Emerging Technologies, 128. ISSN 0968090X.

Li, L., Lin, H., Wan, J., Ma, Z., e Wang, H. (2020). MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualiza- tion.IEEE Access, 8:227113–227125.

Li, W., Tao, W., Qiu, J., Liu, X., Zhou, X., e Pan, Z. (2019).Densely Connected Convolutional Networks With Attention LSTM for Crowd Flows Prediction.IEEE Access, 7:140488–140498.

Li, Y., Chai, S., Ma, Z., e Wang, G. (2021b). A Hybrid Deep Learning Framework for Long-Term Traffic Flow Prediction.IEEE Access, 9:11264–11271. ISSN 21693536.

Liu, H., Lin, Y., Chen, Z., Guo, D., Zhang, J., e Jing, H. (2019a). Research on the Air Traffic Flow Prediction Using a Deep Learning Approach.IEEE Access, 7:148019–148030. ISSN 21693536.

Liu, R., Wang, Y., Zhou, H., e Qian, Z. (2019b). Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine.IEEE Access, 7:158025–158034.

Ma, Y., Zhang, Z., e Ihler, A. (2020). Multi-Lane Short-Term Traffic Forecasting with Convolutional LSTM Network.IEEE Access, 8:34629–34643. ISSN 21693536.

Mena-Oreja, J. e Gozalvez, J. (2021).On the impact of floating car data and data fusion on the prediction of the traffic density, flow and speed using an error recurrent convolutional neural network.IEEE Access, 9:133710–133724. ISSN 21693536.

Mou, L., Zhao, P., Xie, H., e Chen, Y. (2019).T-LSTM: A Long Short-Term Memory Neural Network Enhanced by Temporal Information for Traffic Flow Prediction.IEEE Access, 7:98053– 98060.

Muhammed, T., Mehmood, R., Albeshri, A., e Katib, I. (2018). UbeHealth: A Personalized Ubi- quitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities.IEEE Access, 6:32258–32285.

Nigam, A. e Srivastava, S. (2023). Hybrid deep learning models for traffic stream variables predic- tion during rainfall.Multimodal Transportation, 2:100052. ISSN 27725863.

Olayode, I. O., Tartibu, L. K., e Okwu, M. O. (2021).Prediction and modeling of traffic flow of human-driven vehicles at a signalized road intersection using artificial neural network mo- del: A south african road transportation system scenario.Transportation Engineering, 6. ISSN 2666691X.

Pranolo, A., Mao, Y., Wibawa, A. P., Utama, A. B. P., e Dwiyanto, F. A. (2022). Robust lstm with tuned-pso and bifold-attention mechanism for analyzing multivariate time-series.IEEE Access, 10:78423–78434. ISSN 21693536.

Qi, X., Qin, X., Jia, Z., Lin, M., e Liu, Y. (2020).NCAE and ELM Based Enhanced Ensemble Optimized Model for Traffic Flow Forecasting.IEEE Access, 8:200486–200499.

Reza, S., Ferreira, M. C., Machado, J. J., e Tavares, J. M. R. (2022). A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks.Expert Systems with Applications, 202. ISSN 09574174.

Ruan, H., Wu, B., Li, B., Chen, Z., e Yun, W. (2021). Expressway exit station short-term traffic flow prediction with split traffic flows according originating entry stations.IEEE Access, 9: 86285–86299. ISSN 21693536.

Ruan, T., Wu, D., Chen, T., Jin, C., Xu, L., Zhou, S., e Jiang, Z. (2020). Context-Aware Traffic Prediction Framework Based on Series Decomposition.IEEE Access, 8:202848–202857.

Van Der Bijl, B., Gijsbertsen, B., Van Loon, S., Reurich, Y., De Valk, T., Koch, T., e Dugundji, E. (2022). A comparison of approaches for the time series forecasting of motorway traffic flow rate at hourly and daily aggregation levels.Procedia Computer Science, 201:213–222.

Villarroya, C., Calafate, C. T., Onaindia, E., Cano, J.-C., e Martinez, F. J. (2022). Neural network- based model for traffic prediction in the city of valencia.Procedia Computer Science, 207: 552–562. ISSN 18770509.

Yang, J., Dong, X., e Jin, S. (2020). Metro Passenger Flow Prediction Model Using Attention-Based Neural Network.IEEE Access, 8:30953–30959.

Zang, D., Fang, Y., Wei, Z., Tang, K., e Cheng, J. (2019).Traffic Flow Data Prediction Using Residual Deconvolution Based Deep Generative Network.IEEE Access, 7:71311–71322. ISSN 21693536.

Zhang, J., Chen, F., e Shen, Q. (2019). Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit.IEEE Access, 7:147653–147671. ISSN 21693536.

Zhang, K., Wu, L., Zhu, Z., e Deng, J. (2020a). A Multitask Learning Model for Traffic Flow and Speed Forecasting.IEEE Access, 8:80707–80715.

Zhang, L., Alharbe, N. R., Luo, G., Yao, Z., e Li, Y. (2018). A hybrid forecasting framework based on support vector regression with a modified genetic algorithm and a random forest for traffic flow prediction.Tsinghua Science and Technology, 23(4):479–492. ISSN 18787606.

Zhang, W., Yao, R., Du, X., e Ye, J. (2021). Hybrid deep spatio-temporal models for traffic flow pre- diction on holidays and under adverse weather.IEEE Access, 9:157165–157181. ISSN 21693536.

Zhang, X., Zhang, T., Zou, Y., Du, G., e Guo, N. (2020b).Predictive Eco-Driving Application Considering Real-World Traffic Flow.IEEE Access, 8:82187–82200.

Zhang, Y. e Xin, D. (2020).Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction.IEEE Access, 8:91510–91520.

Zhao, J., Nie, Y., Ni, S., e Sun, X. (2020). Traffic Data Imputation and Prediction: An Efficient Realization of Deep Learning.IEEE Access, 8:46713–46722. ISSN 21693536.

Zhao, W., Gao, Y., Ji, T., Wan, X., Ye, F., e Bai, G. (2019). Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting.IEEE Access, 7:114496–114507.

Zheng, H., Li, X., Li, Y., Yan, Z., e Li, T. (2022). Gcn-gan: Integrating graph convolutional network and generative adversarial network for traffic flow prediction.IEEE Access, 10:94051–94062. ISSN 21693536.

Zheng, J. e Huang, M. (2020). Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning.IEEE Access, 8:82562–82570.

Zhu, K., Xun, P., Li, W., Li, Z., e Zhou, R. (2019). Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning.IEEE Access, 7:142272–142279.

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