Traffic Flow Prediction Using Long-Short Term Memory Technique for Connected Vehicles in Smart Cities

Abstract

An intelligent transportation system is an advanced application that aims to improve the efficiency and safety of various modes of transportation. It works by providing innovative services related to different modes of transportation and traffic management. Machine and deep learning have become an integral part of improving the efficiency of traffic flow prediction. In this study, we proposed a traffic flow prediction using Long-Short Term Memory (LSTM) technique to improve a traffic flow prediction. Our experimental design and algorithm to investigate the accuracy of traffic flow prediction are presented in this paper. For data simulation, the VISSIM simulator is utilised to generate data for classification training and testing. Validation will be done by applying other techniques discussed in the literature. This study will serve as a confirmatory study for traffic flow prediction using LSTM.