Explaining patterns of cycling speed stability and disruption

Cycling speed is an important attribute of bicycle traffic flow, being related to travel times, safety and road capacity. Although cycling speed changes constantly during a trip, it is typically measured at the trip-average or aggregated level, and microscopic speed fluctuations are rarely studied. This study aims to quantitatively understand the cycling speed stability within a trip and the determinants of speed stability and disruption. To this end, data from bicycle trips tracked with GPS devices are used. A change point detection method, the pruned exact linear time (PELT) algorithm, is adapted to split trip trajectories into segments differing in speed stability. Then, a rule-based algorithm is developed to classify segments into six speed (in)stability patterns: stable, increase, decrease, V-shape (speed decreases followed by increases), reverse V-shape (speed increases followed by decreases) and complicated unstable patterns. Finally, a two-level multinomial model is estimated to examine the determinants of different patterns. The findings suggest that stable patterns account for half the trip distances, and their speed is higher than the speed of unstable patterns. The V-shape pattern is the most frequent unstable type. Intersections, turns and built-up land use are the main causes of unstable speeds. Cycling on physically separate paths tends to involve more unstable speeds than on mixed-use infrastructures, such as bicycle streets and bicycle tracks. This study finds that daily cycling involves a considerable amount of unstable speed. While its effects have not been directly examined, speed instability likely increases travel times and physical effort and is perceived negatively by cyclists. This underscores the potential benefits of a smooth cycling network and highlights the need for future research on the role of speed stability.