Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology
Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizin...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3520 |
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| author | Dong-youn Lee Ho-jun Yoo Jae-yong Lee Gyeong-ok Jeong |
| author_facet | Dong-youn Lee Ho-jun Yoo Jae-yong Lee Gyeong-ok Jeong |
| author_sort | Dong-youn Lee |
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| description | Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort. |
| format | Article |
| id | doaj-art-9d0bc5a186214aef91cd1f8baecf0320 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-9d0bc5a186214aef91cd1f8baecf03202025-08-20T02:33:02ZengMDPI AGSensors1424-82202025-06-012511352010.3390/s25113520Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor TechnologyDong-youn Lee0Ho-jun Yoo1Jae-yong Lee2Gyeong-ok Jeong3Department of Road Transport, Korea Transport Institute, Sejong-si 30147, Republic of KoreaResearch Institute, RoadKorea Inc., Yongin-si 18471, Republic of KoreaDepartment of PPP Infrastructure Management, Korea Transport Institute, Sejong-si 30147, Republic of KoreaDepartment of Road Transport, Korea Transport Institute, Sejong-si 30147, Republic of KoreaSurface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort.https://www.mdpi.com/1424-8220/25/11/3520bicycle road roughness index (BRI)faulting impact index (FII)surface roughnesssmartphone sensor technology |
| spellingShingle | Dong-youn Lee Ho-jun Yoo Jae-yong Lee Gyeong-ok Jeong Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology Sensors bicycle road roughness index (BRI) faulting impact index (FII) surface roughness smartphone sensor technology |
| title | Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology |
| title_full | Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology |
| title_fullStr | Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology |
| title_full_unstemmed | Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology |
| title_short | Development of a Bicycle Road Surface Roughness and Risk Assessment Method Using Smartphone Sensor Technology |
| title_sort | development of a bicycle road surface roughness and risk assessment method using smartphone sensor technology |
| topic | bicycle road roughness index (BRI) faulting impact index (FII) surface roughness smartphone sensor technology |
| url | https://www.mdpi.com/1424-8220/25/11/3520 |
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