Institute for Transportation

About the research

The report for this project outlines the development of a comprehensive trail management program designed to improve the maintenance of paved trails in Central Iowa. Using the innovative Iowa Data Bike—a data collection tool developed and built by the Des Moines Area Metropolitan Planning Organization in 2017 that integrates an electric-assist bicycle with a smartphone and a GoPro camera—the program collected detailed data on trail surface roughness and overall conditions. The primary objectives were to establish a standardized metric for trail surface roughness, termed the Bike Roughness Index (BRI), and to develop an automated framework for surface distress evaluation using deep learning techniques.

The BRI was calculated using accelerometer data, while trail conditions were assessed through double integration and whole-body vibration analyses. Additionally, a Mask R-CNN model was trained to detect, classify, and segment various types of trail surface distress, enabling accurate and efficient condition assessments. The results demonstrate that the Iowa Data Bike is effective for rapid data collection and that the BRI is a reliable measure of surface roughness. The deep learning model successfully identified and quantified different distress types, which can significantly aid in maintenance planning.

This integrated approach supports proactive maintenance strategies, ensuring the safety and quality of recreational trails. Moreover, the report provides guidelines for optimal data collection, highlighting the potential for these methods to be refined and applied across diverse trail management contexts.