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<p>Utilitarian cycling speed is a crucial input for applications such as infrastructure design, mode and route choice models, traffic microsimulation, safety evaluations, and health impact assessments. However, current methods fail to distinguish between average speed and cruising speed, the latter of which is more behaviourally indicative. This study aims to identify cruising speed from GPS data and investigate how it varies with contextual and personal factors. We evaluate six algorithms to extract cruising events from cycling GPS travel data: three time series clustering methods to identify steady-state events, in combination with two labeling methods to identify which events represent cruising. The best-performing algorithm uses Toeplitz Inverse Covariance-Based Clustering and identifies cruising events based on a decision tree heuristic. The average cruising speed of 21.53&#xa0;km/hr is significantly higher than the overall average speed of 19.95&#xa0;km/hr. Cruising speeds are higher for commute trips, longer trips, e-cyclists, ‘Dedicated’ cyclists, and men. Regarding route factors, cruising speeds are higher in locations with lower grade, more greenery, on-street cycling facilities, high motor vehicle volume, no traffic controls, and lower relative crash risk. Distinguishing cruising events within cycling trajectory data is necessary to avoid underestimating the behavioural sensitivity of cyclists to factors such as road grade, facility type, relative crash risk, trip purpose, gender, and bicycle motorization.</p>

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Identification and investigation of cruising speeds from cycling GPS data

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Elmira Berjisian,
  • Alexander Bigazzi

摘要

Utilitarian cycling speed is a crucial input for applications such as infrastructure design, mode and route choice models, traffic microsimulation, safety evaluations, and health impact assessments. However, current methods fail to distinguish between average speed and cruising speed, the latter of which is more behaviourally indicative. This study aims to identify cruising speed from GPS data and investigate how it varies with contextual and personal factors. We evaluate six algorithms to extract cruising events from cycling GPS travel data: three time series clustering methods to identify steady-state events, in combination with two labeling methods to identify which events represent cruising. The best-performing algorithm uses Toeplitz Inverse Covariance-Based Clustering and identifies cruising events based on a decision tree heuristic. The average cruising speed of 21.53 km/hr is significantly higher than the overall average speed of 19.95 km/hr. Cruising speeds are higher for commute trips, longer trips, e-cyclists, ‘Dedicated’ cyclists, and men. Regarding route factors, cruising speeds are higher in locations with lower grade, more greenery, on-street cycling facilities, high motor vehicle volume, no traffic controls, and lower relative crash risk. Distinguishing cruising events within cycling trajectory data is necessary to avoid underestimating the behavioural sensitivity of cyclists to factors such as road grade, facility type, relative crash risk, trip purpose, gender, and bicycle motorization.