in this paper, we have made the first contribution by designing two right types of questions, namely Routing Query (RQ) to ask the crowd to decide the direction at each road intersection. Secondly, we propose a series of efficient algorithms to dynamically manage the questions in order to reduce the selection hardness within a limited budget. In particular, we show that there are two factors affecting the informativeness of a question: the randomness (entropy) of the question and the structural position of the road intersection. Furthermore, we extend the framework to enable multiple RQs per round. To ease the pain of the sample sensitiveness, we propose a new approach to reduce the selection hardness by reasoning on a so-called Selective Bayesian network. We compare our approach against several baselines, and the effectiveness and efficiency of our proposal are verified by the results in simulations and experiments on real-world datasets. The experimental results show that, even the Selective Bayesian Network provides only partial information of causality, the performance on the reduction of the selection hardness are dramatically improved, especially when the size of samples are relatively small.