Smart City Gnosys
Smart city article details
| Title | A Traffic Flow Prediction Framework Based On Integrated Federated Learning And Recurrent Long Short-Term Networks |
|---|---|
| ID_Doc | 5610 |
| Authors | Pulligilla M.K.; Vanmathi C. |
| Year | 2024 |
| Published | Peer-to-Peer Networking and Applications, 17, 6 |
| DOI | http://dx.doi.org/10.1007/s12083-024-01792-x |
| Abstract | For smart cities, predicting traffic flow is crucial to lower traffic jams and enhancing transportation efficiency. The smart city needs effective models, highly dependable networks, and data privacy for traffic flow prediction (Traff-FP). The majority of current research uses a central training mode and ignores privacy issue conveyed by distributed traffic data. In this paper, an effective traffic flow prediction (ETraff-FP) is proposed to forecast traffic flow using actual historical traffic data. Initially, pre-processing is carried out using data normalization and handling missing value. The three major components of Traff-FP framework for each local Traff-FP model are recurrent long short-term capture network (RLSCN), federated gated graph attentive network (FGAN) and semantic connection relationship capture network (SCRCN). The long-term spatio and temporal information in each location has been captured by RLSCN, which encompasses constituents like fully connected (FC) layers, convolution, and bidirectional long short term memory (BiLSTM) to collect short-term information. FGAN, which incorporates bi-directional gated recurrent unit (Bi-GRU), exchanges short-term spatio-temporal hidden information while it trains local Traff-FP model using elliptic curve diffie-hellman (ECDiff-H) algorithm. Accordingly, the hyper parameters of ETraff-FP are tuned using extended remora optimization algorithm (EReOA). The ETraff-FP framework is trained and tested with TaxiNYC and TaxiBJ datasets. For simulation, python platform is utilized and various evaluation metrics are analysed. Accordingly, the ETraff-FP framework has reached better improvements with MSE of 8.98% and 10.57%, RMSE of 8.62% and 18.65%, MAE of 2.11% and 10.57%, R2-score of 0.959% and 0.913%, and MAPE of 21.12% and 24.89% against the existing methods using TaxiNYC and TaxiBJ datasets. Overall, the proposed work not only advances the state-of-the-art in traffic flow prediction but also proves the value of enabling effective and efficient traffic management systems in urban and smart city environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. |
| Author Keywords | And Intelligent transportation systems; Edge computing; Federated learning; Recurrent long short-term capture network; Spatio and temporal information; Traffic congestion; Traffic flow prediction |
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