Citation

Ng, Hu and Yap, Timothy Tzen Vun and Yap, Wen Jiun and Goh, Vik Tor and Wong, Jan Shao and Ng, Jin Ren (2019) Identification of Road Surface Conditions using IoT Sensors and Machine Learning. In: Computational Science and Technology. Identification of Road Surface Conditions using IoT Sensors and Machine Learning, 481 . Springer, Lecture Notes in Electrical Engineering, pp. 259-268. ISBN 978-981-13-2622-6

Text 33.pdf - Published Version Restricted to Repository staff only Download (43MB)

Abstract

The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using k-Nearest Neighbour (k-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.

Item Type:	Book Section
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE) Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Sep 2021 02:44
Last Modified:	20 Sep 2021 02:44
URII:	http://shdl.mmu.edu.my/id/eprint/8983

Downloads

Edit (login required)