## 1 Introduction  
The context of modern urbanization and fast-paced life reduces people's daily exercise. Various diseases, such as obesity and cardiovascular diseases, are also increasing. As a convenient way of exercise and travel, cycling not only improves the health of cyclists but is also a sustainable mode of transportation with environmental and social benefits [1]. In recent years, lots of countries have implemented strategies to improve their cycling service and turn it into a safer and more convenient mode of travel [2, 3, 4, 5]. With the proliferation of sports apps and GPS mobile devices in daily life, various sensing devices have recorded a large number of outdoor-recreation cycling trajectories, including Strava, Keep and so on. Generally, such kinds of big data contain rich spatial and temporal information of the cycling in cities, thus opening up valuable opportunities to explore the underlying cycling semantics to enable a better cycling experience. For example, knowing different types of popular cycling (e.g., recreation, commuting) could help cyclists to find the most desirable and suitable routes, and knowing the roads that are difficult to ride could help city planners to build cycling infrastructures in proper locations. Hence, _it is quite meaningful to effectively uncover the implicit cycling semantics from large-scale GPS trajectories._  
Trajectories traverse a series of streets, and street names may imply geographic and cultural information to the citizens of a city [6]. Thus, by converting the cycling trajectories into a corresponding set of street names, the obtained corpus is able to semantically reveal the riders' hidden cycling themes, i.e., the behavior semantics of cyclists. For example,a cyclist often rides along the _Seine_, indicating that he/she is likely to do recreational cycling. Moreover, it is well-recognized that cycling is naturally restricted to the road network. However, existing studies mainly focused on analyzing the cycling from the perspective of cyclists, ignoring the underlying semantics from road segments [7, 8]. In fact, road segments could inherently influence many aspects of cycling, e.g., some roads in poor conditions generally indicate an unfavorable or challenging cycling experience. Fortunately, such influences could also be revealed by GPS trajectories. It is because trajectories contain cyclists' fine-grained moving characteristics on roads, such as smooth or intense, thus explicitly indicating where they are suitable for smooth cycling and where they are challenging for cycling, i.e., the moving semantics on roads. In a nutshell, in this paper, we aim at utilizing large-scale cycling trajectories and road network data _to explore cycling semantics in a city from the perspectives of both cyclists and road segments._  
However, it is still nontrivial to effectively extract and perceive the underlying cycling semantics. The challenges are threefold. Firstly, due to the GPS noise and other positioning deviations, the study on the raw GPS trajectories would suffer from the uncertainty problem. Secondly, the road segments are static in nature while the cycling passing through the road segments is spatially and temporally dynamic. Consequently, it is difficult to aggregate the dynamic moving features of the trajectories into static road segments, and it needs to be decoupled properly. Thirdly, cycling semantics in the city cannot be intuitively presented through statistical information, which is hard to be perceived especially for users with low or no technical backgrounds, e.g., urban planners [9]. To overcome the above challenges, this paper presents a new visual analytic system for the exploration of cycling semantics. Specifically, to overcome the GPS uncertainty problem, we map the GPS trajectory coordinates from geometrical positions to the set of street names, then construct a high-level and meaningful textual corpus.