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Research article - Peer-reviewed, 2022

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

Gebisa, Amanuel; Gebresenbet, Girma; Gopal, Rajendiran; Nallamothu, Ramesh Babu

Abstract

This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC's development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method.

Keywords

Addis Ababa; driving cycle; emissions; neural network; vehicle

Published in

Sustainability
2022, volume: 14, number: 21, article number: 13772
Publisher: MDPI

Authors' information

Gebisa, Amanuel
Adama Science and Technology University
Swedish University of Agricultural Sciences, Department of Energy and Technology
Gopal, Rajendiran
Defence University-College of Engineering
Nallamothu, Ramesh Babu
Adama Science and Technology University

UKÄ Subject classification

Transport Systems and Logistics

Publication Identifiers

DOI: https://doi.org/10.3390/su142113772

URI (permanent link to this page)

https://res.slu.se/id/publ/120172