Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU
This work addresses traffic flow and safety improvements for drivers and traffic management systems, but it is incremental as it applies existing LSTM and GRU methods to a specific domain.
The paper tackled the problem of real-time traffic volume prediction in dynamic and complex conditions by developing memory-based recurrent neural network models, achieving effective predictions as demonstrated on the Metro Interstate Traffic Volume dataset.
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.