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- W4360892431 abstract "Predicting traffic volume in real-time can improve both traffic flow and road safety. A precise traffic volume forecast helps alert drivers to the flow of traffic along their preferred routes, preventing potential deadlock situations. Existing parametric models cannot reliably forecast traffic volume in dynamic and complex traffic conditions. Therefore, in order to evaluate and forecast the traffic volume for every given time step in a real-time manner, we develop non-linear memory-based deep neural network models. Our extensive experiments run on the Metro Interstate Traffic Volume dataset demonstrate the effectiveness of the proposed models in predicting traffic volume in highly dynamic and heterogeneous traffic environments." @default.
- W4360892431 created "2023-03-25" @default.
- W4360892431 creator A5079557230 @default.
- W4360892431 date "2023-03-22" @default.
- W4360892431 modified "2023-10-16" @default.
- W4360892431 title "Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU" @default.
- W4360892431 doi "https://doi.org/10.48550/arxiv.2303.12643" @default.
- W4360892431 hasPublicationYear "2023" @default.
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