Development and Prediction of Kuala Terengganu Driving Cycle via Long Short-Term Memory Recurrent Neural Network

Driving cycle is as representation of traffic behaviour in an area or city It plays a fundamental role in the design of vehicles and to test the performance of the vehicles This paper studies a driving cycle development method based on k means clustering and driving cycle prediction based on Long Short Term Memory LSTM by Recurrent Neural Network RNN The objectives of this paper are to develop a Kuala Terengganu Driving Cycle KTDC by using k means clustering to develop a prediction of future KTDC and lastly to analyse the energy consumption and emissions of KTDC Firstly the driving data is collected in five different routes in Kuala Terengganu city at go to work times Then the data is divided into micro trips and the driving features are extracted The features are used to develop a driving cycle using k means clustering approach The prediction is developed after the training of neural networks by using LSTM network approach Finally the energy consumption and emissions of KTDC is analysed by using AUTONOMIE software 2023 WITPress All rights reserved