Citation

Jun, Wei Lim and Timothy, Tzen Vun Yap and Vik, Tor Goh and Ng, Hu and Wen, Jiun Yap and Thiam, Yong Kuek (2020) Street SAFE - Road Fault Monitoring and Reporting. International Journal of Engineering Trends and Technology. pp. 120-124. ISSN 2231-5381

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Abstract

Maintaining roads have become challenging as road users are on the rise. Tough weather conditions and high traffic make road surfaces deteriorate swiftly. Manual detection on these defects is not efficient. Due to the rise of smartphone use, the accelerometers in the smartphone are employed for road fault classification. Supervised machine learning classification models of data pertaining to pothole, speed bump, hazard line, smooth road, uneven road, turn, and hard stop are trained with the Random Forest (RF) and Support Vector Machine (SVM) algorithms, which is then utilized in StreetSAFE (Smartphone Assisted Fault Examination), a machine learning aided system to detect road faults and report them in real time. Using statistical parameters, the system is found to able to distinguish road surface conditions. The system can potentially predict road damage, facilitate maintenance and resource management.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, statistical features, accelerometer, road faults.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Sep 2021 06:11
Last Modified:	12 Apr 2023 07:42
URII:	http://shdl.mmu.edu.my/id/eprint/8425

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