References
- Bai, J., et al., 2021. A3T-GCN: attention temporal graph convolutional network for traffic forecasting. ISPRS International Journal of Geo-Information, 10 (7), 485.
- Bai, L., et al., 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in Neural Information Processing Systems, 33, 17804–17815.
- Ballakur, A.A. and Arya, A., 2020. Empirical evaluation of gated recurrent neural network architectures in aviation delay prediction. In: 2020 5th International conference on computing, communication and security (ICCCS). IEEE, 1–7.
- Cao, S., et al., 2022. A spatio-temporal sequence-to-sequence network for traffic flow prediction. Information Sciences, 610, 185–203.
- Che, Z., et al., 2018. Recurrent neural networks for multivariate time series with missing values. Scientific Reports, 8 (1), 6085.
- Chen, J., et al., 2024. Heterogeneous graph traffic prediction considering spatial information around roads. International Journal of Applied Earth Observation and Geoinformation, 128, 103709.
- Chen, Z., et al., 2020. Network adjustment: Channel search guided by flops utilization ratio. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.
- Cirstea R-G, et al., 2019. Graph attention recurrent neural networks for correlated time series forecasting. In: MileTS19@ KDD, 1–6.
- Cui, Z., et al., 2020a. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transportation Research Part C: Emerging Technologies, 118, 102674.
- Cui, Z., et al., 2020b. Graph Markov network for traffic forecasting with missing data. Transportation Research Part C: Emerging Technologies, 117, 102671.
- Deng, M., et al., 2023. MVCV-Traffic: multiview road traffic state estimation via cross-view learning. International Journal of Geographical Information Science, 37 (10), 2205–2237.
- Furtlehner, C., et al., 2022. Short-term forecasting of urban traffic using spatio-temporal Markov field. IEEE Transactions on Intelligent Transportation Systems, 23 (8), 10858–10867.
- Gu, Y., et al., 2019. Short-term prediction of lane-level traffic speeds: a fusion deep learning model. Transportation Research Part C: Emerging Technologies, 106, 1–16.
- Guo, K., et al., 2021. Optimized graph convolution recurrent neural network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 22 (2), 1138–1149.
- Guo, S., et al., 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33 (01), 922–929.
- He, S., et al., 2023. STGC-GNNs: a GNN-based traffic prediction framework with a spatial–temporal Granger causality graph. Physica A: Statistical Mechanics and Its Applications, 623, 128913.
- Jin, M., et al., 2023. A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection. arXiv preprint arXiv:2307.03759.
- Lehtola, V.V., et al., 2022. Digital twin of a city: review of technology serving city needs. International Journal of Applied Earth Observation and Geoinformation, 114, 102915.
- Li, F., et al., 2023. Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Transactions on Knowledge Discovery from Data, 17 (1), 1–21.
- Li, Y., et al., 2018. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International conference on learning representations.
- Lu, S., et al., 2021. A combined method for short-term traffic flow prediction based on recurrent neural network. Alexandria Engineering Journal, 60 (1), 87–94.
- Luo, G., et al., 2022. ESTNet: embedded spatial-temporal network for modeling traffic flow dynamics. IEEE Transactions on Intelligent Transportation Systems, 23 (10), 19201–19212.
- Park, C., et al., 2020. ST-GRAT: a novel spatio-temporal graph attention networks for accurately forecasting dynamically changing road speed. In: Proceedings of the 29th ACM international conference on information & knowledge management.
- Rahmani, S., et al., 2023. Graph neural networks for intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems, 24 (8), 8846–8885.
- Ren, Y., et al., 2024. TPLLM: a traffic prediction framework based on pretrained large language models. arXiv preprint arxiv:2403.02221.
- Roy, A., et al., 2021. SST-GNN: simplified spatio-temporal traffic forecasting model using graph neural network. In: Pacific-Asia conference on knowledge discovery and data mining. Cham: Springer International Publishing, 12714.
- Shadbahr, T., et al., 2023. The impact of imputation quality on machine learning classifiers for datasets with missing values. Communications Medicine, 3 (1), 139.
- Shao, Z., et al., 2022. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. Proceedings of the VLDB Endowment, 15 (11), 2733–2746.
- Sun, P., et al., 2020. SSGRU: a novel hybrid stacked GRU-based traffic volume prediction approach in a road network. Computer Communications, 160, 502–511.
- Tang, X., et al., 2020. Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values. Proceedings of the AAAI Conference on Artificial Intelligence, 34 (4), 5956–5963.
- Tian, Y., et al., 2018. LSTM-based traffic flow prediction with missing data. Neurocomputing, 318, 297–305.
- Van den Oord, A., 2016. WaveNet: a generative model for raw audio. In: 9th ISCA speech synthesis workshop, 125.
- Wang, P., et al., 2022a. A hybrid data-driven framework for spatiotemporal traffic flow data imputation. IEEE Internet of Things Journal, 9 (17), 16343–16352.
- Wang, P., et al., 2022b. A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation. International Journal of Geographical Information Science, 36 (6), 1231–1257.
- Wang, P., et al., 2023. Urban traffic flow prediction: a dynamic temporal graph network considering missing values. International Journal of Geographical Information Science, 37 (4), 885–912.
- Wang, Z., et al., 2021. Long-term traffic prediction based on LSTM encoder-decoder architecture. IEEE Transactions on Intelligent Transportation Systems, 22 (10), 6561–6571.
- Wu, Y., et al., 2018. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166–180.
- Wu, Z., et al., 2019. Graph WaveNet for deep spatial-temporal graph modeling. In: The 28th international joint conference on artificial intelligence (IJCAI).
- Wu, Z., et al., 2020. Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 753–763.
- Yu, B., et al., 2018. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th international joint conference on artificial intelligence, 3634–3640.
- Yuan, H., and Li, G., 2021. A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Science and Engineering, 6 (1), 63–85.
- Zafar, N., et al., 2022. Applying hybrid LSTM-GRU model based on heterogeneous data sources for traffic speed prediction in urban areas. Sensors, 22 (9), 3348.
- Zhang, J., et al., 2021. Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis. SN Applied Sciences, 3 (4), 1–13.
- Zhang, Y., et al., 2023. Incorporating multimodal context information into traffic speed forecasting through graph deep learning. International Journal of Geographical Information Science, 37 (9), 1909–1935.
- Zhao, L., et al., 2020. T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21 (9), 3848–3858.
- Zuo, J., et al., 2023. Graph convolutional networks for traffic forecasting with missing values. Data Mining and Knowledge Discovery, 37 (2), 913–947.