Along with the increase in strict regulation of drunk driving behavior in China, the demands for designated driving services have risen in popularity. In the absence of specialized cycling map for the designated drivers who use foldable electric bicycles, they tend to take a detour or are lost on the way to the car owners’ appointed parking places. With gradual popularization of chauffeur services, cycling trajectories generated by designated drivers almost spread all over the city. It provides a chance for inferring the cycling map dedicated to the designated drivers. However, to infer an accurate map using trajectories faces severe challenges stemming from random cycling behaviors of designated drivers, including (i) trajectories contain a lot of noises and incomplete segments, (ii) turning trajectories at minor intersections are very sparse and (iii) trajectories on the roads of distinct shapes are obviously different. To address the above challenges, we propose a three-phase map inference framework, called SAMI, consisting of trajectory refinement, intersection pinpointing, and road curve interlinking. Specifically, cycling behavioral differences from neighbor regions are incorporated into intersection identification process to ensure obtaining high detection precision even when trajectory data is sparse. Further, shape-aware based centerline fitting strategy is put forward to guarantee that inferred road curves are consistent with real road shape as possible. Finally, extensive comparative experiments on two real data sets demonstrate that SAMI significantly outperforms state-of-the-art methods by 13.31% in F1-score of map inference and by 44.88% in recall rate of minor intersection detection.