Mobile crowd sensing has become an emerging computing and sensing paradigm that recruits ordinary participants to perform sensing tasks. With the highly dynamic mobility pattern and the abundance of on-board resources, vehicles have been increasingly recruited to participate large-scale crowd sensing applications such as urban sensing. However, existing participant recruitment algorithms take a long time in recruitment decision for large number of vehicular participants. In this paper, a fast algorithm for vehicle participant recruitment problem is proposed, which achieves linear-time complexity at the sacrifice of a slightly lower sensing quality. The participant recruitment problem is modeled as a unconstrained maximization problem without explicitly cost constraint and a trade-off parameter is introduced to control the recruiter cost. Trace-driven simulations on both real-world and synthetic data-sets are conducted to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm is 50 times faster than the state-of-art algorithm at the sacrifice of 5% lower sensing quality when the number of participants is over 1000.