Qaasid News

Download Our App

Author: admin

  • Toshiba launches 3-phase BLDC controller

    In recent years, there has been an increasing trend to use IPM types in preference to SPM types for three-phase BLDC motors to achieve low cost, high output, and high torque.

    Typical applications…

    Continue Reading

  • Harry Styles’ tour could see girlfriend Zoë Kravitz making a cameo: Inside their love story

    Harry Styles’ tour could see girlfriend Zoë Kravitz making a cameo: Inside their love story

    The pair first sparked romance rumors last August, strolling hand-in-hand through Rome, and have since been spotted together in London, New York, and other cities, keeping things low-key. Even Kravitz’s father, Lenny, is said to have given the…

    Continue Reading

  • PGSFormer: traffic flow prediction based on joint optimization of progressive graph convolutional networks with subseries transformer

  • Liu, Y., James, J. Q., Kang, J., Niyato, D. & Zhang, S. Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet Things J. 7 (8), 7751–7763. https://doi.org/10.1109/JIOT.2020.2974820 (2020).

    Google Scholar 

  • Peng, H. et al. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Inf. Sci. 578, 401–416. https://doi.org/10.1016/j.ins.2021.06.053 (2021).

    Google Scholar 

  • Djenouri, Y., Belhadi, A., Srivastava, G. & Lin, J. C. W. Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting.Future Generation Comput. Syst. 139: 100–108. https://doi.org/10.1016/j.future.2022.09.032. (2023).

  • Williams, B. M. & Hoel, L. A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering 129(6), 664–672. https://doi.org/10.1061/(ASCE)0733-947X (2003).

  • Zhang, Q., Li, C., Su, F. & Li, Y. Spatiotemporal residual graph attention network for traffic flow forecasting. IEEE Internet Things J. 10 (13), 11518–11532. https://doi.org/10.1109/JIOT.2023.3248874 (2023).

    Google Scholar 

  • Yu, B., Yin, H. & Zhu, Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3634–3640. https://doi.org/10.24963/ijcai.2018/505 (2018).

  • Li, Y., Yu, R., Shahabi, C. & Liu, Y. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. InternationalConference on Learning Representations (ICLR 2018). Vancouver, Canada. https://doi.org/10.48550/arXiv.1707.01926 (2018).

  • Cai, L., Janowicz., K., Mai., G., Yan., B. & Zhu, R. Traffic transformer: capturing the continuity and periodicity of time series for traffic forecasting. Trans. GIS. 24 (3), 736–755. https://doi.org/10.1111/tgis.12644 (2020).

    Google Scholar 

  • Liu, H. et al. STAEformer: Spatio-Temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting. Proc. 32nd ACM Int. Conf. Inform. Knowl. Manage. (CIKM). 4125-4129 https://doi.org/10.48550/arXiv.2308.10425 (2023).

  • Kazemi, S. M. et al. Representation learning for dynamic graphs: A survey. J. Mach. Learn. Res. 21 (70), 1–73 (2020).

    Google Scholar 

  • Zhao, L. et al. T-GCN: A Temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21 (9), 3848–3858. https://doi.org/10.1109/TITS.2019 (2019).

    Google Scholar 

  • Peng, H. et al. Spatial Temporal incidence dynamic graph neural networks for traffic flow forecasting. Inf. Sci. 521, 277–290. https://doi.org/10.1016/j.ins.2020.02.006 (2020).

    Google Scholar 

  • Vaswani, A. et al. Attention is all you need. Adv. Neural. Inf. Process. Syst. 30, 1–11 (2017).

    Google Scholar 

  • Cai, L., Janowicz, K., Mai, G., Yan, B. & Zhu, R. Traffic transformer: capturing the continuity and periodicity of time series for traffic forecasting. Trans. GIS. 24 (3), 736–755. https://doi.org/10.1111/tgis.12607 (2020).

    Google Scholar 

  • Zheng, C. et al. Spatio-temporal joint graph convolutional networks for traffic forecasting. IEEE Trans. Knowl. Data Eng. 36 (1), 372–385. https://doi.org/10.1109/TKDE.2022 (2023).

    Google Scholar 

  • Han, L., Du, B., Sun, L., Fu, Y. & Lv, Y. and H. Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 547–555 https://doi.org/10.1145/3447548.3467118 (2021).

  • Kong, J., Fan, X., Zuo, M., Deveci, M., Zhong, K. & X., and ADCT-Net: adaptive traffic forecasting neural network via dual-graphic cross-fused transformer. Inform. Fusion. 103, 102122. https://doi.org/10.1016/j.inffus.2023.102122 (2024).

    Google Scholar 

  • Wu, C. H., Ho, J. M. & Lee, D. T. Travel-time prediction with support vector regression. IEEE Trans. Intell. Transp. Syst. 5 (4), 276–281. https://doi.org/10.1109/TITS.2004.837813 (2004).

    Google Scholar 

  • Liu, A. & Zhang. Y Spatial–Temporal dynamic graph convolutional network with interactive learning for traffic forecasting. IEEE Trans. Intell. Transp. Syst. 25 (7), 7645–7660. https://doi.org/10.1109/TITS.2024.3362145 (2024).

    Google Scholar 

  • Wu, Y., Tan, H., Qin, L., Ran, B. & Jiang, Z. A hybrid deep learning based traffic flow prediction method and its Understanding. Transp. Res. Part. C: Emerg. Technol. 90, 166–180. https://doi.org/10.1016/j.trc.2018.03.001 (2018).

    Google Scholar 

  • Li, M. & Zhu, Z. Spatial-temporal fusion graph neural networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 35(5), 4189–4196 https://doi.org/10.1609/aaai.v35i5.16550 (2021).

  • Fang, Z., Long, Q. & Song, G. and K. Spatial-temporal graph ODE networks for traffic flow forecasting. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining: 364–373. (2021). https://doi.org/10.1145/3447548.3467141

  • Xu, Y. et al. Generic dynamic graph convolutional network for traffic flow forecasting. Inform. Fusion. 100, 101946. https://doi.org/10.1016/j. inffus.2023.101946 (2023).

    Google Scholar 

  • Fang, Y., Zhao, F., Qin, Y., Luo, H. & Wang, C. Learning all dynamics: traffic forecasting via locality-aware spatio-temporal joint transformer. IEEE Trans. Intell. Transp. Syst. 23 (12), 23433–23446. https://doi.org/10.1109/TITS.2022 (2022).

    Google Scholar 

  • Lin, L., Li, W., Bi, H. & Qin, L. Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms. IEEE Intell. Transp. Syst. Mag. 14 (2), 197–208. https://doi.org/10.1109/MITS.2021.3058034 (2021).

    Google Scholar 

  • Guo, S., Lin, Y., Wan, H., Cong, G. & L., and Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowl. Data Eng. 34 (11), 5415–5428. https://doi.org/10.1109/TKDE.2021.3081562 (2021).

    Google Scholar 

  • Zhu, J. et al. KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting. IEEE Trans. Intell. Transp. Syst. 23 (9), 15055–15065. https://doi.org/10.1109/TITS.2021.3137177 (2022).

    Google Scholar 

  • Kong, X., Wang, K., Hou, M., Karmakar, F., Li, J. & G., and Exploring human mobility for multi-pattern passenger prediction: A graph learning framework. IEEE Trans. Intell. Transp. Syst. 23 (9), 16148–16160. https://doi.org/10.1109/TITS.2021 (2022).

    Google Scholar 

  • Chen, Y., Segovia, I. & Gel, Y. R. Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting. Proceedings of the 38th International Conference on Machine Learning 139: 1684–1694. (2021).

  • Lee, K. & Rhee, W. DDP-GCN: Multi-graph convolutional network for Spatiotemporal traffic forecasting. Transp. Res. Part. C: Emerg. Technol. 134, 103466. https://doi.org/10.1016/j.trc.2021.103466 (2022).

    Google Scholar 

  • Shin, Y. & Yoon, Y. Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting. IEEE Trans. Intell. Transp. Syst. 23 (3), 2082–2092. https://doi.org/10.1109/TITS.2021 (2022).

    Google Scholar 

  • Bai, L., Yao, L., Li, C., Wang, X. & Wang, C. Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural. Inf. Process. Syst. 33, 17804–17815 (2020).

    Google Scholar 

  • Wu, Z., Pan, S., Long, G., Jiang, J. & Zhang, C. Graph WaveNet for deep spatial-temporal graph modeling. Proceedings of the 28th International Joint Conference on Artificial Intelligence: 1907–1913. (2019). https://doi.org/10.24963/ijcai.2019/264

  • Wang, W. D. P. K. Spatial–Temporal graph attention gated recurrent transformer network for traffic flow forecasting. IEEE Internet Things J. 11 (8), 14267–14281. https://doi.org/10.1109/JIOT.2023.3340182 (2024).

    Google Scholar 

  • Shin, Y. & Yoon. Y PGCN: progressive graph convolutional networks for Spatial-Temporal traffic forecasting. IEEE Trans. Intell. Transp. Syst. 25 (7), 7633–7644. https://doi.org/10.1109/TITS.2024.3349565 (2024).

    Google Scholar 

  • Fang, Y. et al. Efficient large-scale traffic forecasting with transformers: A Spatial data management perspective. KDD ‘25: Proc. 31st ACM SIGKDD Conf. Knowl. Discovery Data Min. 307-317 https://doi.org/10.1145/3690624.3709177 (2024).

  • Fang, Y. et al. Unraveling spatio-temporal foundation models via the pipeline lens: A comprehensive review. Inf. Fusion. 115, 102346. https://doi.org/10.48550/arXiv.2506.01364 (2025).

    Google Scholar 

  • Yang., S., Wu., Q., Wang., Y. & Lin, T. SSGCRTN: A space-specific graph convolutional recurrent transformer network for traffic prediction. Appl. Intell. 54 (22), 11978–11994. https://doi.org/10.1007/s10489-024-05815-1 (2024).

    Google Scholar 

  • Yang., S., Huang., Z., Wu., Q. & Zhuo, Z. MSTDFGRN: A multi-view spatio-temporal dynamic fusion graph recurrent network for traffic flow prediction. Comput. Electr. Eng. 123, 110046. https://doi.org/10.1016/j.compeleceng.2024.110046 (2025).

    Google Scholar 

  • Yang, S., Wu, Q., Li, Z. & Wang, K. PSTCGCN: principal spatio-temporal causal graph convolutional network for traffic flow prediction. Neural Comput. Appl. 1-14, https://doi.org/10.1007/s00521-024-10769-6 (2024).

  • Yang., S., Wu., Q., Li., M. & Sun, Y. Temporal identity interaction dynamic graph convolutional network for traffic forecasting. IEEE Internet Things J. 12 (11), 15057–15072. https://doi.org/10.1109/JIOT.2025.3503328 (2025).

    Google Scholar 

  • Yang., S. & Wu, Q. SDSINet: A Spatiotemporal dual-scale interaction network for traffic prediction. Appl. Soft Comput. 112892. https://doi.org/10.1016/j.asoc.2025.112892 (2025).

    Google Scholar 

  • Yang., S. & Wu, Q. MTEGCRN: Multi-scale Temporal enhanced graph convolutional recurrent network for traffic prediction. Neurocomputing 131064 https://doi.org/10.1016/j.neucom.2025.131064 (2025).

  • Yang., S., Wu., Q., Huang., Z. & Zhuo, Z. General Decoupled Graph Convolutional Recurrent Network for Traffic Prediction. IEEE Sensors J. (2025).

  • Yang., S., Wu., Q. & Li, M. Decoupled Multi-Spatio-Temporal Fusion Graph Convolutional Recurrent Network for Traffic Prediction. Eng. Appl. Artif. Intell. 163: 112956. https://doi.org/10.1016/j.engappai.2025.112956. (2025).

  • Guo, S., Lin, Y., Feng, N., Song, C. & Wan, H. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33(01): 922–929. (2019). https://doi.org/10.1609/aaai.v33i01. 3301922.

  • Lan, S., Huang, M. Y., Wang, W., Yang, W. & Li, H. P. DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. in: International Conference on Machine Learning, PMLR, 11906–11917. (2022).

  • Li, J. D. S. J. W. R. & Huang, Y. Y., Yang. Y.-B. Trafformer: unify time and space in traffic prediction. in: Proceedings of the AAAI Conference on Artificial Intelligence, 37(7): 8114–8122. (2023).

  • Shao, Z., Zhang, Z., Wang, F., Wei, W. & Xu, Y. Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. Proceedings of the 31st ACM International Conference on Information & Knowledge Management: 4454–4458. (2022). https://doi.org/10.1145/3511808.3557410

  • Cui, Z., Henrickson, K., Ke, R. & Wang, Y. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst. 21 (11), 4883–4894. https://doi.org/10.1109/TITS.2019.2950416 (2020).

    Google Scholar 

  • Pan, Z. et al. Spatio-temporal meta learning for urban traffic prediction. IEEE Trans. Knowl. Data Eng. 34 (3), 1462–1476. https://doi.org/10.1109/TKDE.2020.2989138 (2020).

    Google Scholar 

  • Chen, C., Liu, Y., Chen, L. & Zhang, C. Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting. IEEE Trans. Neural Networks Learn. Syst. 34 (10), 6913–6925. https://doi.org/10.1109/TNNLS.2022.3156673 (2022).

    Google Scholar 

  • Al-Huthaif, R., Li, T., Al-Huda, Z. & Li, C. FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network. Inf. Sci. 667 (2024), 120482. https://doi.org/10.1016/j.ins.2024.120482 (2024).

    Google Scholar 

  • Lai, Q. & Chen, P. LEISN: A long explicit-implicit spatio-temporal network for traffic flow forecasting. Expert Syst. Appl. 245 (2024), 123139. https://doi.org/10.1016/j.eswa.2024.123139 (2024).

    Google Scholar 

  • Wang., R., Xi., L., Ye., J., Zhang., F. & Xu, X. Y. L. Adaptive Spatio-Temporal relation based transformer for traffic flow prediction. IEEE Trans. Veh. Technol. 74 (2), 2220–2230. https://doi.org/10.1109/TVT.2024.3390997 (2025).

    Google Scholar 

  • Fan., J., Weng., W., Chen., Q., Wu., H. & Wu, J. Pdg2seq: periodic dynamic graph to sequence model for traffic flow prediction. Neural Netw. 183, 106941. https://doi.org/10.1016/j.neunet.2024.106941 (2025).

    Google Scholar 

  • Wu, B. et al. DT-CTFP: 6 g-enabled digital twin collaborative traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 26 (10), 18129–18144. https://doi.org/10.1109/TITS.2025.3582356 (2025).

    Google Scholar 

Continue Reading

  • Epstein files show art market financialisation in full flow

    Epstein files show art market financialisation in full flow

    If you happen to be near Basel in Switzerland, you could do worse than go and visit the Beyeler Foundation’s exhibition of late works by Cezanne. It’s “eloquent”, according to the FT’s art critic, with paintings which “opened the door…

    Continue Reading

  • ‘Doomsday Glacier’ is melting faster than we thought. Can a 150-metre wall stop it flooding Earth?

    ‘Doomsday Glacier’ is melting faster than we thought. Can a 150-metre wall stop it flooding Earth?

    A global group of scientists, engineers and policy experts has unveiled an ambitious plan to build a wall along the ‘Doomsday Glacier’ as flooding fears continue to grow.

    Located on the West Antarctic Ice Sheet, Thwaites Glacier earned its…

    Continue Reading

  • Chrome Add-On Caught Stealing Amazon Commissions – TechRepublic

    1. Chrome Add-On Caught Stealing Amazon Commissions  TechRepublic
    2. Beware! Fake ChatGPT browser extensions are stealing your login credentials  Bitdefender
    3. Hundreds of Thousands at Risk: Security Experts Flag 17 Chrome and Edge Extensions as Potential…

    Continue Reading

  • Coaches confirmed for Force’s maiden Super Rugby Next Gen campaign

    Coaches confirmed for Force’s maiden Super Rugby Next Gen campaign

    Former
    Western Force players Jeremy Thrush, Jonno Lance and Chris Heiberg will head up
    the Club’s coaching staff for the maiden Super Rugby Next Gen campaign which
    starts on Saturday.

    The Force
    will compete in the new competition featuring…

    Continue Reading

  • ‘Shout if you’re hurting and get the help you need’ – The Irish Times

    ‘Shout if you’re hurting and get the help you need’ – The Irish Times

    I had a jab today, oh boy. I have a jab every four weeks. Lanreotide is a drug you may not be familiar with, but it’s designed to quell neuroendocrine tumours (NETs) secreting hormones into my system. You might not be familiar with why anything…

    Continue Reading

  • Pakistan sends helicopters, drones to end desert standoff; 58 dead – Reuters

    1. Pakistan sends helicopters, drones to end desert standoff; 58 dead  Reuters
    2. How Balochistan attacks threaten Pakistan’s promises to China, Trump  Al Jazeera
    3. Nearly 200 terrorists killed in Balochistan clearance ops  Dawn
    4. China strongly condemns…

    Continue Reading

  • 3 Brands Rewriting Berlin’s Retail Scene

    3 Brands Rewriting Berlin’s Retail Scene

    When people think of shopping in Berlin, they picture vintage stores in Kreuzberg or Neukölln, niche concept spaces, or weekend markets, rather than flagship luxury boutiques. International houses have long operated mono-brand outposts in the…

    Continue Reading