Driving Data Analysis for the Development of Kuala Terengganu Driving Cycle

Anida, IN; Norbakyah, JS; Yussof, WNJHW; Walker, P; Salisa, AR

Driving Data Analysis for the Development of Kuala Terengganu Driving Cycle

Anida, IN Norbakyah, JS Yussof, WNJHW Walker, P

Salisa, AR

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Technological Advancement in Instrumentation & Human Engineering, 2023, 882, pp. 3-14
Issue Date:: 2023-08-11

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Field	Value	Language
dc.contributor.author	Anida, IN
dc.contributor.author	Norbakyah, JS
dc.contributor.author	Yussof, WNJHW
dc.contributor.author	Walker, P https://orcid.org/0000-0003-3988-3966
dc.contributor.author	Salisa, AR
dc.date.accessioned	2023-11-09T04:09:02Z
dc.date.available	2023-11-09T04:09:02Z
dc.date.issued	2023-08-11
dc.identifier.citation	Technological Advancement in Instrumentation & Human Engineering, 2023, 882, pp. 3-14
dc.identifier.isbn	978-981-19-1576-5
dc.identifier.uri	http://hdl.handle.net/10453/173268
dc.description.abstract	Driving cycle plays a vital role in the production and evaluating the performance of the vehicle. Driving cycle is a speed-time data set and as an important input for vehicle emission models. A problem coming with the second-by-second speed driving data is to analyze the big driving data. To analyze this big data, it is necessary to choose best big data analysis methods, which give opportunity to store, preprocess, detect outlier and apply classification or clustering algorithms. In this study, a set of driving data is stored, managed and analyzed using Tall Arrays (TA) and k-means clustering algorithms in MATLAB for the development of Kuala Terengganu Driving Cycle (KTDC). The objectives of this paper are; to store and manage driving data using TA in MATLAB, to develop a KTDC by using k-means clustering, and lastly to analyze 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 stored and analyzed in the MATLAB. The development of KTDC is by using k-means clustering approach. Finally, the energy consumption and emissions of KTDC is analyzed by using AUTONOMIE software. KTDC is successfully developed with 35.15 km/h in average speed and 12 micro-trips.
dc.format.extent	55
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Technological Advancement in Instrumentation & Human Engineering
dc.relation.ispartofseries	Lecture Notes in Electrical Engineering
dc.relation.isbasedon	10.1007/978-981-19-1577-2_1
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Driving Data Analysis for the Development of Kuala Terengganu Driving Cycle
dc.type	Chapter
utslib.citation.volume	882
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2023-11-09T04:09:01Z
pubs.place-of-publication	Singapore
pubs.publication-status	Published
pubs.volume	882
dc.location	Singapore

Abstract:

Driving cycle plays a vital role in the production and evaluating the performance of the vehicle. Driving cycle is a speed-time data set and as an important input for vehicle emission models. A problem coming with the second-by-second speed driving data is to analyze the big driving data. To analyze this big data, it is necessary to choose best big data analysis methods, which give opportunity to store, preprocess, detect outlier and apply classification or clustering algorithms. In this study, a set of driving data is stored, managed and analyzed using Tall Arrays (TA) and k-means clustering algorithms in MATLAB for the development of Kuala Terengganu Driving Cycle (KTDC). The objectives of this paper are; to store and manage driving data using TA in MATLAB, to develop a KTDC by using k-means clustering, and lastly to analyze 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 stored and analyzed in the MATLAB. The development of KTDC is by using k-means clustering approach. Finally, the energy consumption and emissions of KTDC is analyzed by using AUTONOMIE software. KTDC is successfully developed with 35.15 km/h in average speed and 12 micro-trips.

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http://hdl.handle.net/10453/173268