Bachir, Hussein (Faculty of Computer Science and Mathematics, OTH Regensburg), Weikl, Simone (OTH Regensburg)

Identifying Cyclist Riding Styles Using Drone-Based Trajectory Data and Volatility Clustering in Free-Flow Traffic Conditions

Scheduled for presentation during the Regular Session "S06b-Safety, Sensing, and Infrastructure Design for Vulnerable Road Users" (WE-EA-T6), Wednesday, November 19, 2025, 14:50−15:30, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

Cycling is increasingly promoted as a sustainable and healthy urban transportation mode. However, understanding cyclists’ behavior remains underdeveloped compared to car drivers’ behavior research. This paper proposes a two-level unsupervised clustering framework to identify cyclist riding styles in free flow conditions from drone-recorded trajectory data. The first level classifies local riding behavior at the timestamp level using riding volatility measures. The second level identifies overall cyclist behavior through clustering entropy measures, mean rotation fluctuation and surrounding traffic density at the trajectory level. The method was applied to a dataset of 284 trajectories and 100,000 data points from a 500-meter urban road segment in Munich, Germany and revealed two riding patterns on both levels: stable and volatile. The findings highlight the influence of road design clarity and behavioral variability and provide a foundation for cyclist-focused behavior and infrastructure analysis.