Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors
The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle pat...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/22/7210 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850267111324123136 |
|---|---|
| author | Tufail Ahmed Ali Pirdavani Geert Wets Davy Janssens |
| author_facet | Tufail Ahmed Ali Pirdavani Geert Wets Davy Janssens |
| author_sort | Tufail Ahmed |
| collection | DOAJ |
| description | The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths. |
| format | Article |
| id | doaj-art-a2cd327b7b854f3e8a7d59d615b2ff6d |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a2cd327b7b854f3e8a7d59d615b2ff6d2025-08-20T01:53:56ZengMDPI AGSensors1424-82202024-11-012422721010.3390/s24227210Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light SensorsTufail Ahmed0Ali Pirdavani1Geert Wets2Davy Janssens3UHasselt, The Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, BelgiumUHasselt, The Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, BelgiumUHasselt, The Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, BelgiumUHasselt, The Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, BelgiumThe quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths.https://www.mdpi.com/1424-8220/24/22/7210bicycle vibrationcomfort assessmentbicycling comfortsmartphone sensorssmart bicycle lights |
| spellingShingle | Tufail Ahmed Ali Pirdavani Geert Wets Davy Janssens Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors Sensors bicycle vibration comfort assessment bicycling comfort smartphone sensors smart bicycle lights |
| title | Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors |
| title_full | Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors |
| title_fullStr | Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors |
| title_full_unstemmed | Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors |
| title_short | Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors |
| title_sort | evaluating bicycle path roughness a comparative study using smartphone and smart bicycle light sensors |
| topic | bicycle vibration comfort assessment bicycling comfort smartphone sensors smart bicycle lights |
| url | https://www.mdpi.com/1424-8220/24/22/7210 |
| work_keys_str_mv | AT tufailahmed evaluatingbicyclepathroughnessacomparativestudyusingsmartphoneandsmartbicyclelightsensors AT alipirdavani evaluatingbicyclepathroughnessacomparativestudyusingsmartphoneandsmartbicyclelightsensors AT geertwets evaluatingbicyclepathroughnessacomparativestudyusingsmartphoneandsmartbicyclelightsensors AT davyjanssens evaluatingbicyclepathroughnessacomparativestudyusingsmartphoneandsmartbicyclelightsensors |