Investigating the use of machine learning for smart parking applications

Jonathan Barker; Sabih Rehman

doi:10.1109/KSE.2019.8919291

Investigating the use of machine learning for smart parking applications

Jonathan Barker, Sabih Rehman

Charles Sturt University

Research output: Book chapter/Published conference paper › Conference paper › peer-review

6 Citations (Scopus)

Abstract

Traffic congestion caused by greater competition for limited parking spaces in the world’s major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine Learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.

Original language	English
Title of host publication	Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering
Subtitle of host publication	KSE 2019
Editors	Josiane Mothe, Le Hoang Son, Nguyen Tran Quoc Vinh
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781728130026
ISBN (Print)	9781728130033
DOIs	https://doi.org/10.1109/KSE.2019.8919291 https://doi.org/10.1109/KSE.2019.8919291
Publication status	Published - 07 Dec 2019
Event	The 11th IEEE International Conference on Knowledge and Systems Engineering (KSE 2019) - University of Education and Science, The University of Danang, Da Nang, Viet Nam Duration: 24 Oct 2019 → 26 Oct 2019 http://kse2019.ued.udn.vn/welcome http://kse2019.ued.udn.vn/sites/default/files/KSE%202019%20Program%20Ver13-23-10-2019%20-%20print.pdf (program)

Publication series

Name	Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019

Conference

Conference	The 11th IEEE International Conference on Knowledge and Systems Engineering (KSE 2019)
Country/Territory	Viet Nam
City	Da Nang
Period	24/10/19 → 26/10/19
Other	The 11th KSE is an international forum for presentation, discussion, and exchange of the state-of-the-art research, development, and applications in the field of knowledge and systems engineering. The main objective of the KSE is to bring together researchers, practitioners and students not only to share research results and practical applications but also to foster collaboration in the field. The KSE this year will be held in Da Nang, a coastal and tourism city with beautiful beaches and a lot of exciting experiences. The conference will be co-organized by The University of Da Nang - University of Science and Education (UD-USE) and VNU University of Engineering and Technology (VNU-UET).
Internet address	http://kse2019.ued.udn.vn/welcome http://kse2019.ued.udn.vn/sites/default/files/KSE%202019%20Program%20Ver13-23-10-2019%20-%20print.pdf (program)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Barker, J., & Rehman, S. (2019). Investigating the use of machine learning for smart parking applications. In J. Mothe, L. H. Son, & N. T. Quoc Vinh (Eds.), Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering : KSE 2019 (pp. 1-5). Article 8919291 (Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/KSE.2019.8919291, https://doi.org/10.1109/KSE.2019.8919291

Barker, Jonathan ; Rehman, Sabih. / Investigating the use of machine learning for smart parking applications. Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering : KSE 2019. editor / Josiane Mothe ; Le Hoang Son ; Nguyen Tran Quoc Vinh. IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1-5 (Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019).

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title = "Investigating the use of machine learning for smart parking applications",

abstract = "Traffic congestion caused by greater competition for limited parking spaces in the world{\textquoteright}s major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine Learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.",

keywords = "Machine learning, Prediction algorithms, Machine learning alogorithms, Automobiles, Intelligent sensors, Real-time systems",

author = "Jonathan Barker and Sabih Rehman",

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doi = "10.1109/KSE.2019.8919291",

language = "English",

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booktitle = "Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering",

address = "United States",

note = "The 11th IEEE International Conference on Knowledge and Systems Engineering (KSE 2019) ; Conference date: 24-10-2019 Through 26-10-2019",

url = "http://kse2019.ued.udn.vn/welcome, http://kse2019.ued.udn.vn/sites/default/files/KSE%202019%20Program%20Ver13-23-10-2019%20-%20print.pdf",

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Barker, J & Rehman, S 2019, Investigating the use of machine learning for smart parking applications. in J Mothe, LH Son & NT Quoc Vinh (eds), Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering : KSE 2019., 8919291, Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019, IEEE, Institute of Electrical and Electronics Engineers, pp. 1-5, The 11th IEEE International Conference on Knowledge and Systems Engineering (KSE 2019), Da Nang, Viet Nam, 24/10/19. https://doi.org/10.1109/KSE.2019.8919291, https://doi.org/10.1109/KSE.2019.8919291

Investigating the use of machine learning for smart parking applications. / Barker, Jonathan; Rehman, Sabih.
Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering : KSE 2019. ed. / Josiane Mothe; Le Hoang Son; Nguyen Tran Quoc Vinh. IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1-5 8919291 (Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Investigating the use of machine learning for smart parking applications

AU - Barker, Jonathan

AU - Rehman, Sabih

PY - 2019/12/7

Y1 - 2019/12/7

N2 - Traffic congestion caused by greater competition for limited parking spaces in the world’s major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine Learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.

AB - Traffic congestion caused by greater competition for limited parking spaces in the world’s major cities is a growing problem. To overcome this challenge, a study has been carried out to use a smart parking application that utilises machine learning algorithms to help predict future car parking occupancy rates at Port Macquarie campus of Charles Sturt University (CSU), Australia. Parking data was collected over a five-week period and the WEKA Machine Learning Workbench was used to identify high-performing algorithms for predicting future parking occupancy rates. In the initial phase, some well known algorithms were used to investigate occupancy rates. In the next phase of the study, student class timetable data was used to enhance prediction accuracy and investigate parking occupancy trends. While most algorithms proved to be accurate in stable conditions, the KStar algorithm appeared to produce better results during highly variable conditions.

KW - Machine learning

KW - Prediction algorithms

KW - Machine learning alogorithms

KW - Automobiles

KW - Intelligent sensors

KW - Real-time systems

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SN - 9781728130033

T3 - Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019

SP - 1

EP - 5

BT - Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering

A2 - Mothe, Josiane

A2 - Son, Le Hoang

A2 - Quoc Vinh, Nguyen Tran

PB - IEEE, Institute of Electrical and Electronics Engineers

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Y2 - 24 October 2019 through 26 October 2019

ER -

Barker J, Rehman S. Investigating the use of machine learning for smart parking applications. In Mothe J, Son LH, Quoc Vinh NT, editors, Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering : KSE 2019. IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 1-5. 8919291. (Proceedings of 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019). doi: 10.1109/KSE.2019.8919291, 10.1109/KSE.2019.8919291

Investigating the use of machine learning for smart parking applications

Abstract

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UN SDGs

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