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

Amanuel Gebisa, Girma Gebresenbet (), Rajendiran Gopal and Ramesh Babu Nallamothu
Additional contact information
Amanuel Gebisa: Mechanical Engineering Department, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia
Girma Gebresenbet: Division of Automation and Logistics, Department of Energy and Technology, Swedish University of Agricultural Science, P.O. Box 7032, 750 07 Uppsala, Sweden
Rajendiran Gopal: Department of Motor Vehicle Engineering, Defence University-College of Engineering, Bishoftu P.O. Box 1041, Ethiopia
Ramesh Babu Nallamothu: Mechanical Engineering Department, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia

Sustainability, 2022, vol. 14, issue 21, 1-27

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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