Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors

Area of Science:

• Civil Engineering
• Transportation Engineering
• Urban Planning

Background:

• Bicycle path surface quality directly impacts cyclist comfort and safety.
• Existing methods for assessing infrastructure quality can be costly and time-consuming.
• There is a need for efficient, scalable solutions to monitor cycling infrastructure.

Purpose of the Study:

• To evaluate the effectiveness of smartphone sensors and smart bicycle lights in assessing bicycle path roughness.
• To compare the accuracy of data from smartphone applications and smart bicycle lights for measuring vibrational comfort.
• To determine the feasibility of using these technologies for large-scale infrastructure assessment.

Main Methods:

• Utilized smartphone sensors (Physics Toolbox Sensor Suite) and smart bicycle lights (SEE.SENSE) to collect GPS and vertical acceleration data on various Belgian bicycle paths.
• Calculated Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from collected data to quantify cyclist vibrational comfort.
• Performed statistical comparisons of comfort assessment results from different data sources.

Main Results:

• No significant statistical difference was found in the comfort assessment between the Dynamic Comfort Index (DCI), Root Mean Square (RMS) values, and SEE.SENSE data.
• Smartphone sensors and smart bicycle lights provide comparable data for evaluating bicycle path surface quality.
• The study demonstrated the viability of these technologies for objective comfort measurements.

Conclusions:

• Smartphone sensors and smart bicycle lights are effective, low-cost tools for assessing bicycle path roughness and cyclist comfort.
• Integrating these technologies enables efficient, large-scale monitoring of cycling infrastructure.
• Findings support improved urban planning and enhanced cycling conditions through continuous infrastructure quality assessment.