Surface Type Estimation from GPS Tracked Bicycle Activities

TL;DR

This paper presents a simple, GPS-based method for classifying road surface types for bicycles, achieving high accuracy with machine learning models, and offering a cost-effective alternative to satellite image analysis.

Contribution

It introduces a novel, computationally inexpensive approach using GPS data and machine learning to classify road surface types for bicycles, outperforming previous satellite-based methods.

Findings

Decision trees achieved 86% accuracy in classification.

GPS-based methods are effective alternatives to satellite image analysis.

Multiple algorithms were tested, with decision trees performing best.

Abstract

Road conditions affect both machine and human powered modes of transportation. In the case of human powered transportation, poor road conditions increase the work for the individual to travel. Previous estimates for these parameters have used computationally expensive analysis of satellite images. In this work, we use a computationally inexpensive and simple method by using only GPS data from a human powered cyclist. By estimating if the road taken by the user has high or low variations in their directional vector, we classify if the user is on a paved road or on an unpaved trail. In order to do this, three methods were adopted, changes in frequency of the direction of slope in a given path segment, fitting segments of the path, and finding the first derivative and the number of points of zero crossings of each segment. Machine learning models such as support vector machines, K-nearest neighbors, and decision trees were used for the classification of the path. We show in our methods, the decision trees performed the best with an accuracy of 86\%. Estimation of the type of surface can be used for many applications such as understanding rolling resistance for power estimation estimation or building exercise recommendation systems by user profiling as described in detail in the paper.