Traditional road maintenance methods are costly; requiring expensive equipment and manpower. Road quality categorization based on machine learning techniques, using real-time opportunistic data gathered from inexpensive open-source inertial systems, is a promising alternative. Existing open-source datasets for this problem are small and less representative of actual situation where data is imbalanced and skewed towards regular road surface instances. With the help of an inexpensive device and data collection platform developed by our lab, we have collected a large, heterogeneous dataset which is more realistic representative of the problem in real world settings. There are four kinds of Roadway Surface Disruptions (RSDs) considered in this work, namely, Cat eyes, Manholes, Potholes and Speed bumps. The feature set used consists of spectral features, time-series peaks, statistical features such as Kurtosis and Skewness and cepstral features such as Mel Frequency Cepstral Coefficients(MFCC). Feature selection was conducted using Sequential Forward Selection and Relief Algorithm. Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forest (RF) and Nave Bayes (NB) were used for classification. The best results are reported by SVM with the True Positive Rate (TPR) of 95.2%. These anomaly classification results can be used as a low-cost road maintenance solution by road repairing authorities and the road quality maps thus generated can provide the passengers and drivers with the information of most comfortable route for their journey. Hence, the proposed unified classification framework provides a solution to both of the target audiences by considering relevant anomalies.