Assessing and Mapping of Road Surface Roughness based on GPS and Accelerometer Sensors on Bicycle-Mounted Smartphones

Abstract

The surface roughness of roads is an essential road characteristic. Due to the employed carrying platforms (which are often cars), existing measuring methods can only be used for motorable roads. Until now, there has been no effective method for measuring the surface roughness of un-motorable roads, such as pedestrian and bicycle lanes. This hinders many applications related to pedestrians, cyclists and wheelchair users. In recognizing these research gaps, this paper proposes a method for measuring the surface roughness of pedestrian and bicycle lanes based on Global Positioning System (GPS) and accelerometer sensors on bicycle-mounted smartphones. We focus on the International Roughness Index (IRI), as it is the most widely used index for measuring road surface roughness. Specifically, we analyzed a computing model of road surface roughness, derived its parameters with GPS and accelerometers on bicycle-mounted smartphones, and proposed an algorithm to recognize potholes/humps on roads. As a proof of concept, we implemented the proposed method in a mobile application. Three experiments were designed to evaluate the proposed method. The results of the experiments show that the IRI values measured by the proposed method were strongly and positively correlated with those measured by professional instruments. Meanwhile, the proposed algorithm was able to recognize the potholes/humps that the bicycle passed. The proposed method is useful for measuring the surface roughness of roads that are not accessible for professional instruments, such as pedestrian and cycle lanes. This work enables us to further study the feasibility of crowdsourcing road surface roughness with bicycle-mounted smartphones.

Analyzing Road Coverage of Public Vehicles According to Number and Time Period for Installation of Road Inspection Systems

“…Therefore, in recent years, the development of inexpensive smartphone-based measurement systems has been actively conducted. Kaiyue et al [23] and Bump Recorder [24] proposed systems that use smartphones to measure the International Roughness Index (IRI), which is an important road condition indicator. Yagi [25] proposed a method of measuring road flatness using the accelerometer of a smartphone.…”

SmarterRoutes – Data-driven road complexity estimation for level-of-detail adaptation of navigation services

“…Logging acceleration and monitoring changes over time might provide valuable contributions towards overall complexity estimation. Zang et al (Zang et al, 2018) proposed a method for road surface roughness estimation using a mobile application. Their work is basically showing the possibility to get road surface insights from smartphone acceleration sensors.…”

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

“…Extensive attention has been paid to the analysis of these signals and their multimodal processing with further biomedical data [7,8] for feature extraction, classification, and human-computer interactions. Methods of motion detection and its analysis by accelerometers and global positioning systems (GPS) are also used for studies of physical activities including cycling [9][10][11][12][13][14], as assessed in this paper.…”

“…There are very wide applications of motion monitoring systems, including gait analysis [21][22][23][24][25][26], motion evaluation [27][28][29][30][31][32], stroke patients monitoring [18,33], recognition of physical activities [34][35][36][37][38], breathing [39], and detection of motion disorders during sleep [40]. The combination of accelerometer sensors at different body locations in possible combination with GPS and geographical information systems is useful in improving movement monitoring of humans [41], assessing road surface roughness [10], and activity recognition [30] as well.…”