Almost every today's smartphone is integrated with many useful sensors. The sensors are originally designed to make the smartphones' user interface and applications more convenient and appealing. These sensors, moreover, are potentially useful for many other applications in different fields. Using smartphone sensors to estimate road roughness condition has a great potential, since many similar sensors are already in use in many sophisticated road roughness profilers. This study explores the use of data, collected by sensors from smartphones under realistic settings, in which the smartphones are placed at more realistic locations and under realistic manner inside a moving vehicle, to evaluate its relationship with the actual road pavement roughness. An experiment has been conducted to collect data from smartphone acceleration and Global Positioning System (GPS) sensors; frequency domain analysis is also carried out. It has been revealed that the data from smartphone accelerometers has a linear relationship with road roughness condition, whereas the strength of the relationship varies at different frequency ranges. The results of this paper also confirm that smartphone sensors have a great potential to be used for estimating the current status of the road pavement condition.
Efficient road infrastructure maintenance and management depends on many factors, of which the availability of updated pavement condition data is among the most important. Today's smartphones, which usually come with many sensors, are potentially useful tools for pavement condition estimation. This paper explores the use of data from smartphones' accelerometers to analyze for features and relationship of acceleration vibration to estimate road roughness condition. Although, the estimation might not be as accurate as modern profilers, it still may be very useful for cost saving and as an indicator for continuous monitoring. In the experiment, smartphones are placed inside vehicles and drive along selected road sections to gather data for analysis. The analysis consists of data filtering, matching with location and reference data, sectioning and frequency domain analysis. Results show that acceleration vibration magnitude has a linear relationship with road roughness condition.
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