Traffic flow prediction using deep learning

Abstract

An Intelligent Transport System (ITS) model that is contingent on newlinethe compulsion and expertise of the Traffic Prediction System in the newlinecontemporary urban context is proposed in this research work. Deep Learning newline(DL) is computationally becoming comfortable to train and set as many newlinehyperparameters automatically as possible. The researchers and practitioners newlinecrave to set as many hyperparameters inevitably as possible in the DL. To be a newlinegreat enabler ITS has to find suitable solutions to issues like alert on live time newlinetraffic information to interested parties along with facility to retrieve on demand newlinethe long-term statistical data, reduce the middling waiting time for commuters, newlineoffer protected, consistent, value-added services, control with vitality the signal newlinetiming based on the traffic flow etc., All these limitations call for instant newlineattention. The problems like the sharp nonlinearities caused by transitions newlinebetween free flow, breakdown, retrieval, and congestion stand out among all of newlinethe other issues that have been listed. newlineThe contributions in this research work are: newline(i) Adopt an ascendable approach to kindle the scarce newlineinformation formed; newline(ii) Exploit the attention mechanism to exterminate the newlinedisadvantages of Long Short-Term Memory (LSTM) newlinemethods for traffic prediction; newline(iii) Suggest a new fusion smoothing model; newline(iv) Investigating, developing and utilizing the Bayesian newlinecontextual bandits ; newlineiv newline(v) Recommend a Linear model based on LSTM, in conjunction newlinewith Bayesian contextual bandits. The travel speed prediction newlineis done by LSTM. newlineThe results authenticate that the proposed model can adeptly achieve newlinethe goal of developing a system. The proposed model is definitely the best newlinesolution to overcome the issues. The results further demonstrate that the newlineproposed model can successfully develop a system that is dependent on the newlineTraffic Prediction System in the present urban context, as the first layer of the newlinemodel identifies spatiotemporal relations among predictors and the other layers newlinemodel non