Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach

Abstract

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

IoT for measuring road network quality index

“…Supervised classification was accomplished by supplying a set of input data (vehicle pitch' vehicle roll' and forward and lateral acceleration) and two classes (no defect' speed bumps). In [26], the authors proposed a real-time road pavement conditions monitoring using vibration-based data. They used short-time Fourier analysis to extract signal energy information.…”

A Vibration-Based Methodology to Monitor Road Surface: A Process to Overcome the Speed Effect

“…The premise is that when a vehicle circulates on a distressed road surface (e.g., characterized by potholes, cracking, and unmaintained manholes), it produces more vibrations compared to a perfectly maintained road pavement surface [5]. This is the principle used both for the evaluation of standardized index evaluation, such as the IRI index [7][8][9][10], and also with reference to non-standardized procedures and indices [11][12][13][14][15][16].…”

Evaluating the Influence of Data Entropy in the Use of a Smart Equipment for Traffic Management at Border Check Point

“…The last part of the research and the main objective of the paper is the assessment of the influence of data uncertainty over the service parameters of the system designed and evaluated by simulation. The incorrect data obtained from sensors that are used to measure the vehicles weight or to recognize the plate number must be identified and corrected or entered in the modeling as a percentage of incorrect data [17,18]. In this approach the uncertainty is accepted, and the simulation model is used to evaluate the impact of it.…”