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.