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Unmanned aerial vehicles (UAVs) serve as vital tools for collecting real-time traffic data, enabling rapid responses to events, and optimizing traffic management based on their cost-effectiveness, high flexibility, and wide coverage range advantages. The urban UAV cruising problem primarily focuses on selecting the locations of fixed automated station (FAS) and optimizing UAVs' cruising routes. This paper introduces a strategy where UAVs can take off and land on different FASs to enhance urban UAVs' cruising efficiency. Firstly, the problem is formulated as an integrated linear integer model (FU-ILP) which simultaneously determines the locations of FASs and the routes of UAVs. The objective is to minimize redundant cruising distance and cruising cycle time. The FU-ILP model takes into account UAV's flight capabilities, FAS's signal coverage range, power requirements, and battery charging. Moreover, we design a tailored branch-and-price (TB&P) algorithm to decompose the FU-ILP model, which can reduce the variable scale for solving larger-scale cases. Finally, we test the model and the algorithm on the Sioux Falls Test Network. The TB&P algorithm improves computational efficiency by nearly 80%. Moreover, our strategy yields superior results compared to the strategy where UAVs must return to their take-off FASs for recharging. We also demonstrate that increasing the number of UAVs does not reduce redundant cruising distance in the whole network, but it can shorten the cruising cycle. These policy conclusions also apply to the case of Xiongan. This provides a basis for managers to calculate the minimum UAV's configuration based on the cities' actual cruising frequency requirements. [ABSTRACT FROM AUTHOR]