TY  - JOUR
T1  - Classification of Road Surface Conditions Using Vehicle Positional Dynamics
AU - Tzen Vun Yap, Timothy AU - Ng, Hu AU - Tor Goh, Vik AU - Weng Seah, Jeng 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 3
SP  - 501
EP  - 507
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.501.507
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.501.507
KW  - Road surface condition
KW  -feature selection
KW  -classification
KW  -vehicle positional dynamics
KW  -machine Learning
AB  - The objective of this research is to collect and analyze road surface conditions in Malaysia and
develop a classification model that can identify road surface conditions from the collected data. Data is
collected through a mobile application that collects positional dynamics of vehicles on the road. Features
considered include statistical measures such as minimum, maximum, standard deviation, median, average,
skewness and kurtosis. Selection of the extracted features is performed using Ranker, Tabu search and Particle
Swarm Optimization (PSO) followed by classification using k-Nearest Neighborhood (k-NN) Random Forest (RF)
and Support Vector Machine (SVM) with linear, Radial Basis Function (RBF) and polynomial kernels. The
classification model that gave the highest accuracy is SVM (RBF) with a Correct Classification Rate (CCR) of
91.71%. Trailing closely was RF at 91.17%. Although not as accurate as SVM, the difference was negligible and
its computational time was much lower than the former. In the feature selection process, features which provide
positive contribution to the classification process were chosen and the best performances were produced by
PSO with an average CCR of 89.88%. Tabu selected 11 features while PSO selected 13 features where the extra
two features made a difference in the results. Ranker selected every single feature but has the lowest average
CCR. This is attributed to a subset of features that were selected were ineffectively impeding the classification.
The features and classification model employed were able to effectively classify road surface conditions from
vehicle positional dynamics. Using only 3D positional readings of the vehicle and standard statistical measures,
road surface conditions can be effectively identified for the prioritisation and facilitation of road maintenance.
ER  -