Smooth cycling can improve the competitiveness of bicycles. Understanding cycling speed variation during a trip reveals the infrastructure or situations which promote or prevent smooth cycling. However, research on this topic is still limited. This study analyses speed variation based on data collected in the Netherlands, using GPS-based devices, continuously recording geographical positions and thus the variation in speeds during trips. Linking GPS data to spatial data sources adds features that vary during the trip. Multilevel mixed-effects models were estimated to test the influence of factors at cyclist, trip and tracking point levels. Results show that individuals who prefer a high speed have a higher average personal speed. Longer trips and trips made by conventional electric bicycles and sport bicycles have a higher average trip speed. Tracking point level variables explain intra-trip cycling speed variations. Light-medium precipitation and tailwind increase cycling speed, while both uphill and downhill cycling is relatively slow. Cycling in natural and industrial areas is relatively fast. Intersections, turns and their adjacent roads decrease cycling speed. The higher the speed, the stronger the influence of infrastructure on speed. Separate bicycle infrastructure, such as bike tracks, streets and lanes, increase speed. These findings are useful in the areas of cycling safety, mode choice models and bicycle accessibility analysis. Furthermore, these findings provide additional evidence for smooth cycling infrastructure construction.

平稳的骑行可以提高自行车的竞争力。了解旅途中骑行速度的变化可以揭示促进或阻碍骑行平稳的基础设施或情况。然而，关于这一主题的研究仍然有限。本研究根据在荷兰收集的数据分析速度变化，使用基于 GPS 的设备，连续记录地理位置，从而分析出行期间的速度变化。将 GPS 数据链接到空间数据源可添加在旅途中变化的功能。估计多级混合效应模型来测试骑车人、行程和跟踪点级别因素的影响。结果表明，喜欢高速的人平均个人速度较高。较长的行程以及传统电动自行车和运动自行车的行程具有较高的平均行程速度。跟踪点水平变量解释了行程内骑行速度的变化。轻中度降水和顺风会增加骑行速度，而上坡和下坡骑行都相对较慢。在自然区和工业区骑自行车速度相对较快。交叉路口、转弯处及其邻近道路会降低骑行速度。速度越高，基础设施对速度的影响越强。独立的自行车基础设施，例如自行车道、街道和车道，可以提高速度。这些发现对于骑行安全、模式选择模型和自行车可达性分析等领域非常有用。此外，这些发现为自行车基础设施建设的顺利进行提供了额外的证据。