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

Abstract

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.

Enhanced Fuel Efficiency via Eco-Driving Training in Freight Transport

“…However, the environmental ramifications of transportation, especially greenhouse gas emissions stemming from fossil fuel combustion, are nonnegligible. Vehicular exhaust, suboptimal driving practices, and traffic-related pollution are significant contributors to urban environmental degradation [2]. Given that all transportation modalities are inherently energy-dependent, the escalation of renewable energy adoption is paramount in curtailing environmental detriments.…”