TY - GEN
T1 - Identification of road surface conditions using IoT sensors and machine learning
AU - Ng, Jin Ren
AU - Wong, Jan Shao
AU - Goh, Vik Tor
AU - Yap, Wen Jiun
AU - Yap, Timothy Tzen Vun
AU - Ng, Hu
N1 - Funding Information:
cation, Malaysia, under the Fundamental Research Grant Scheme with grant number FRGS/1/2015/SG07/MMU/02/1, as well as the Multimedia University Capex Fund with Project ID MMUI/CAPEX170008, are gratefully acknowledged.
Funding Information:
Acknowledgements. Financial support from the Ministry of Higher Edu-
Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Classification
KW - Machine learning
KW - Road surface conditions
UR - http://www.scopus.com/inward/record.url?scp=85053275669&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-2622-6_26
DO - 10.1007/978-981-13-2622-6_26
M3 - Conference contribution
AN - SCOPUS:85053275669
SN - 9789811326219
T3 - Lecture Notes in Electrical Engineering
SP - 259
EP - 268
BT - Computational Science and Technology
A2 - Alfred, Rayner
A2 - Ibrahim, Ag Asri Ag
A2 - Lim, Yuto
A2 - Anthony, Patricia
PB - Springer
T2 - 5th International Conference on Computational Science and Technology 2018
Y2 - 29 August 2018 through 30 August 2018
ER -